construct.ipynb

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   "source": [
    "# Part 1: Construct a KG from unstructured data\n",
    "\n",
    "This notebook illustrates the internal steps for the implementation of an NLP pipeline for constructing _knowledge graphs_ from unstructured data sources.\n",
    "\n",
    "Presentation for GraphGeeks.org on 2024-08-14"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a1438b6-9e0d-4fea-872e-2a9d3b7631c8",
   "metadata": {},
   "source": [
    "## Set up"
   ]
  },
  {
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   "source": [
    "from IPython.display import display, HTML, Image, SVG\n",
    "\n",
    "from collections import defaultdict\n",
    "from dataclasses import dataclass\n",
    "import itertools\n",
    "import os\n",
    "import typing\n",
    "import warnings\n",
    "\n",
    "from gliner_spacy.pipeline import GlinerSpacy\n",
    "from icecream import ic\n",
    "from pydantic import BaseModel\n",
    "from pyinstrument import Profiler\n",
    "import glirel\n",
    "import matplotlib\n",
    "import matplotlib.colors\n",
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import pyvis\n",
    "import spacy\n",
    "import transformers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79bde75b-ef58-4f98-b581-228f89339a91",
   "metadata": {},
   "source": [
    "Override specific Hugging Face error messages, since `transformers` and `tokenizers` have noisy logging."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f8267417-8b99-4574-8c12-f71129fc99da",
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   "outputs": [],
   "source": [
    "transformers.logging.set_verbosity_error()\n",
    "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"0\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b4d04b7-d90b-48f2-8b0b-eae64bdfcc9c",
   "metadata": {},
   "source": [
    "Show a watermark of the OS, hardware, language environment, and dependent library versions."
   ]
  },
  {
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   "execution_count": 3,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: 2024-08-12T13:28:05.782806-07:00\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.11.9\n",
      "IPython version      : 8.26.0\n",
      "\n",
      "Compiler    : Clang 13.0.0 (clang-1300.0.29.30)\n",
      "OS          : Darwin\n",
      "Release     : 23.5.0\n",
      "Machine     : arm64\n",
      "Processor   : arm\n",
      "CPU cores   : 14\n",
      "Architecture: 64bit\n",
      "\n",
      "pandas      : 2.2.2\n",
      "pyvis       : 0.3.2\n",
      "networkx    : 3.3\n",
      "glirel      : 0.1.4\n",
      "matplotlib  : 3.9.1\n",
      "transformers: 4.41.2\n",
      "spacy       : 3.7.5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark\n",
    "%watermark --iversions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8455b153-9fa2-4e4d-a7ed-b039b4d0ac96",
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   "source": [
    "Start the stochastic stack trace profiler."
   ]
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   "outputs": [],
   "source": [
    "profiler: Profiler = Profiler()\n",
    "profiler.start()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5b7159a-d1f3-4616-8513-6b3f1185b874",
   "metadata": {},
   "source": [
    "Define the model selections and parameter settings."
   ]
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   "source": [
    "CHUNK_SIZE: int = 1024\n",
    "\n",
    "GLINER_MODEL: str = \"urchade/gliner_small-v2.1\"\n",
    "\n",
    "NER_LABELS: typing.List[ str] = [\n",
    "    \"Behavior\",\n",
    "    \"City\",\n",
    "    \"Company\",    \n",
    "    \"Condition\",\n",
    "    \"Conference\",\n",
    "    \"Country\",\n",
    "    \"Food\",\n",
    "    \"Food Additive\",\n",
    "    \"Hospital\",\n",
    "    \"Organ\",\n",
    "    \"Organization\",\n",
    "    \"People Group\",\n",
    "    \"Person\",\n",
    "    \"Publication\",\n",
    "    \"Research\",\n",
    "    \"Science\",\n",
    "    \"University\",\n",
    "]\n",
    "\n",
    "RE_LABELS: dict = {\n",
    "    \"glirel_labels\": {\n",
    "        \"no_relation\": {},  \n",
    "        \"co_founder\": {\"allowed_head\": [\"PERSON\"], \"allowed_tail\": [\"ORG\"]}, \n",
    "        \"country_of_origin\": {\"allowed_head\": [\"PERSON\", \"ORG\"], \"allowed_tail\": [\"LOC\", \"GPE\"]}, \n",
    "        \"parent\": {\"allowed_head\": [\"PERSON\"], \"allowed_tail\": [\"PERSON\"]}, \n",
    "        \"followed_by\": {\"allowed_head\": [\"PERSON\", \"ORG\"], \"allowed_tail\": [\"PERSON\", \"ORG\"]},  \n",
    "        \"spouse\": {\"allowed_head\": [\"PERSON\"], \"allowed_tail\": [\"PERSON\"]},  \n",
    "        \"child\": {\"allowed_head\": [\"PERSON\"], \"allowed_tail\": [\"PERSON\"]},  \n",
    "        \"founder\": {\"allowed_head\": [\"PERSON\"], \"allowed_tail\": [\"ORG\"]},  \n",
    "        \"headquartered_in\": {\"allowed_head\": [\"ORG\"], \"allowed_tail\": [\"LOC\", \"GPE\", \"FAC\"]},  \n",
    "        \"acquired_by\": {\"allowed_head\": [\"ORG\"], \"allowed_tail\": [\"ORG\", \"PERSON\"]},  \n",
    "        \"subsidiary_of\": {\"allowed_head\": [\"ORG\"], \"allowed_tail\": [\"ORG\", \"PERSON\"]}, \n",
    "    }\n",
    "}\n",
    "\n",
    "SPACY_MODEL: str = \"en_core_web_md\"\n",
    "\n",
    "STOP_WORDS: typing.Set[ str ] = set([\n",
    "    \"PRON.it\",\n",
    "    \"PRON.that\",\n",
    "    \"PRON.they\",\n",
    "    \"PRON.those\",\n",
    "    \"PRON.we\",\n",
    "    \"PRON.which\",\n",
    "    \"PRON.who\",\n",
    "])\n",
    "\n",
    "TR_ALPHA: float = 0.85\n",
    "TR_LOOKBACK: int = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1d8fb60-8b0b-4238-8ef7-e0ff79234387",
   "metadata": {},
   "source": [
    "Load the models for `spaCy`, `GLiNER`, `GLiREL` -- this may take several minutes when run the first time."
   ]
  },
  {
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       "Fetching 4 files:   0%|          | 0/4 [00:00<?, ?it/s]"
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   ],
   "source": [
    "nlp: spacy.Language = spacy.load(SPACY_MODEL)\n",
    "\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "\n",
    "    nlp.add_pipe(\n",
    "        \"gliner_spacy\",\n",
    "        config = {\n",
    "            \"gliner_model\": GLINER_MODEL,\n",
    "            \"labels\": NER_LABELS,\n",
    "            \"chunk_size\": CHUNK_SIZE,\n",
    "            \"style\": \"ent\",\n",
    "        },\n",
    "    )\n",
    "        \n",
    "    nlp.add_pipe(\n",
    "        \"glirel\",\n",
    "        after = \"ner\",\n",
    "    );"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee68eb24-2de1-4821-b68f-3ec6296dd434",
   "metadata": {},
   "source": [
    "Define the global data structures -- which need to be reset for every run, not for each chunk iteration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8428135c-6560-482d-a470-dfeb49a5ac55",
   "metadata": {
    "execution": {
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   },
   "outputs": [],
   "source": [
    "graph: nx.Graph = nx.Graph()\n",
    "known_lemma: typing.List[ str ] = []"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d45acde6-2fc4-4a07-b43e-ce240070b3e1",
   "metadata": {},
   "source": [
    "## Parse one text chunk"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4d2f9d4-8ea4-4260-984b-a5439ede386c",
   "metadata": {},
   "source": [
    "Define an input text chunk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fb077276-ae16-4aff-9ede-63ccdea9a7ae",
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   "source": [
    "class TextChunk (BaseModel):\n",
    "    uid: int\n",
    "    url: str\n",
    "    text: str\n",
    "    \n",
    "SAMPLE_CHUNK: TextChunk = TextChunk(\n",
    "    uid = 1,\n",
    "    url = \"https://www.theguardian.com/society/article/2024/jul/31/eating-processed-red-meat-could-increase-risk-of-dementia-study-finds\",\n",
    "    text = \"\"\"\n",
    "Eating processed red meat could be a significant risk factor for dementia, according to a large study that tracked more than 100,000 people over four decades.\n",
    "Processed red meat has previously been shown to increase the risk of cancer, heart disease and type 2 diabetes. Now US researchers say they have uncovered a potential link to dementia.\n",
    "The study also found that replacing processed red meat with healthier foods such as nuts, beans or tofu could help reduce the risk of dementia. The findings were presented at the Alzheimer’s Association international conference in the US.\n",
    "The number of people living with dementia globally is forecast to nearly triple to 153 million by 2050, and studies looking at diet and risk of cognitive decline has become a focus of researchers.\n",
    "\n",
    "In the latest research, experts studied the health of 130,000 nurses and other health workers working in the US. They were tracked for 43 years and provided data on their diet every 2 to 5 years.\n",
    "The participants were asked how often they ate processed red meat including bacon, hotdogs, sausages, salami and other sandwich meat.\n",
    "They were also asked about their consumption of nuts and legumes including peanut butter, peanuts, walnuts and other nuts, string beans, beans, peas, soy milk and tofu.\n",
    "\n",
    "More than 11,000 cases of dementia were identified during the follow-up period.\n",
    "Consuming two servings of processed red meat each week appeared to raise the risk of cognitive decline by 14% compared with those eating about three servings a month, the researchers reported.\n",
    "The study also suggested that replacing one daily serving of processed red meat for a daily serving of nuts, beans or tofu every day could lower the risk of dementia by 23%.\n",
    "    \"\"\".strip(),\n",
    ")\n",
    "\n",
    "chunk: TextChunk = SAMPLE_CHUNK"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "494f97d2-96eb-464a-97d7-9561d4db8528",
   "metadata": {},
   "source": [
    "Parse the input text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "af4871d9-8113-4e31-bb28-94325f144bc9",
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    "execution": {
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   },
   "outputs": [],
   "source": [
    "doc: spacy.tokens.doc.Doc = list(\n",
    "    nlp.pipe(\n",
    "        [( chunk.text, RE_LABELS )],\n",
    "        as_tuples = True,\n",
    "    )\n",
    ")[0][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "943baa84-74e6-40f4-95f2-dfe3034a39eb",
   "metadata": {},
   "source": [
    "Visualize the `spaCy` parse and `GLiNER` _named entity recognition_ results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1f3da3f0-bb24-46b2-a818-fc47eedfd925",
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    "scrolled": true
   },
   "outputs": [
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     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">Eating \n",
       "<mark class=\"entity\" style=\"background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    processed red meat\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Food</span>\n",
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       " could be a significant risk factor for \n",
       "<mark class=\"entity\" style=\"background: #ddd; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    dementia\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">Condition</span>\n",
       "</mark>\n",
       ", according to a large study that tracked more than 100,000 people over four decades.<br></div></span>"
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      "text/plain": [
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       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1625\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1800\">for</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1800\">ADP</tspan>\n",
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       "\n",
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       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1975\">NOUN</tspan>\n",
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       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
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       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2150\">VERB</tspan>\n",
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       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2325\">to</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2325\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
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       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2500\">DET</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
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       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2675\">ADJ</tspan>\n",
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       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
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       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
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       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3025\">PRON</tspan>\n",
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       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
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       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3200\">VERB</tspan>\n",
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       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
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       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3375\">ADJ</tspan>\n",
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       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3550\">than</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3550\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3725\">100,000</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3725\">NUM</tspan>\n",
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       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
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       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3900\">NOUN</tspan>\n",
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       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
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       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4075\">ADP</tspan>\n",
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       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
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       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4250\">NUM</tspan>\n",
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       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
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       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"572.0\">\n",
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       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4600\">SPACE</tspan>\n",
       "</text>\n",
       "\n",
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       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M1120,529.0 L1112,517.0 1128,517.0\" fill=\"currentColor\"/>\n",
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       "\n",
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       "    <path class=\"displacy-arrowhead\" d=\"M1295,529.0 L1287,517.0 1303,517.0\" fill=\"currentColor\"/>\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M1470,529.0 L1462,517.0 1478,517.0\" fill=\"currentColor\"/>\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M1615.0,529.0 L1623.0,517.0 1607.0,517.0\" fill=\"currentColor\"/>\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "    <path class=\"displacy-arrowhead\" d=\"M1775.0,529.0 L1783.0,517.0 1767.0,517.0\" fill=\"currentColor\"/>\n",
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       "\n",
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       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1275\">DET</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1450\">week</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1450\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1625\">appeared</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1625\">VERB</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1800\">to</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1800\">PART</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"1975\">raise</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"1975\">VERB</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2150\">the</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2150\">DET</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2325\">risk</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2325\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2500\">of</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2500\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2675\">cognitive</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2675\">ADJ</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"2850\">decline</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"2850\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3025\">by</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3025\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3200\">14%</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3200\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3375\">compared</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3375\">VERB</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3550\">with</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3550\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3725\">those</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3725\">PRON</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3900\">eating</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3900\">VERB</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4075\">about</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4075\">ADV</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4250\">three</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4250\">NUM</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4425\">servings</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4425\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4600\">a</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4600\">DET</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4775\">month,</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4775\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4950\">the</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4950\">DET</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"5125\">researchers</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"5125\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"5300\">reported.</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"5300\">VERB</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"484.5\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"5475\">\n",
       "</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"5475\">SPACE</tspan>\n",
       "</text>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "    </text>\n",
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       "\n",
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       "    </text>\n",
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       "\n",
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       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M385.0,441.5 L393.0,429.5 377.0,429.5\" fill=\"currentColor\"/>\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M555.0,441.5 L563.0,429.5 547.0,429.5\" fill=\"currentColor\"/>\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M770,441.5 L762,429.5 778,429.5\" fill=\"currentColor\"/>\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-6107be5397cf475c9eee33c975584f14-0-5\" stroke-width=\"2px\" d=\"M945,439.5 C945,352.0 1080.0,352.0 1080.0,439.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
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       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M945,441.5 L937,429.5 953,429.5\" fill=\"currentColor\"/>\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-6107be5397cf475c9eee33c975584f14-0-6\" stroke-width=\"2px\" d=\"M595,439.5 C595,177.0 1090.0,177.0 1090.0,439.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
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       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M1090.0,441.5 L1098.0,429.5 1082.0,429.5\" fill=\"currentColor\"/>\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-6107be5397cf475c9eee33c975584f14-0-7\" stroke-width=\"2px\" d=\"M1295,439.5 C1295,352.0 1430.0,352.0 1430.0,439.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
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       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M1295,441.5 L1287,429.5 1303,429.5\" fill=\"currentColor\"/>\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-6107be5397cf475c9eee33c975584f14-0-8\" stroke-width=\"2px\" d=\"M1470,439.5 C1470,352.0 1605.0,352.0 1605.0,439.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
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       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M1470,441.5 L1462,429.5 1478,429.5\" fill=\"currentColor\"/>\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-6107be5397cf475c9eee33c975584f14-0-9\" stroke-width=\"2px\" d=\"M1645,439.5 C1645,2.0 5300.0,2.0 5300.0,439.5\" fill=\"none\" stroke=\"currentColor\"/>\n",
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       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M1645,441.5 L1637,429.5 1653,429.5\" fill=\"currentColor\"/>\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M1820,441.5 L1812,429.5 1828,429.5\" fill=\"currentColor\"/>\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "    <path class=\"displacy-arrowhead\" d=\"M1960.0,441.5 L1968.0,429.5 1952.0,429.5\" fill=\"currentColor\"/>\n",
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       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3725\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"3900\">every</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"3900\">DET</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4075\">day</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4075\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4250\">could</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4250\">AUX</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4425\">lower</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4425\">VERB</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4600\">the</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4600\">DET</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4775\">risk</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4775\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"4950\">of</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"4950\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"5125\">dementia</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"5125\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"5300\">by</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"5300\">ADP</tspan>\n",
       "</text>\n",
       "\n",
       "<text class=\"displacy-token\" fill=\"currentColor\" text-anchor=\"middle\" y=\"747.0\">\n",
       "    <tspan class=\"displacy-word\" fill=\"currentColor\" x=\"5475\">23%.</tspan>\n",
       "    <tspan class=\"displacy-tag\" dy=\"2em\" fill=\"currentColor\" x=\"5475\">NOUN</tspan>\n",
       "</text>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "        <textPath xlink:href=\"#arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-0\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">det</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M70,704.0 L62,692.0 78,692.0\" fill=\"currentColor\"/>\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "        <textPath xlink:href=\"#arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-1\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">nsubj</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M245,704.0 L237,692.0 253,692.0\" fill=\"currentColor\"/>\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-2\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">advmod</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M420,704.0 L412,692.0 428,692.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-3\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">mark</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M770,704.0 L762,692.0 778,692.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-4\" stroke-width=\"2px\" d=\"M945,702.0 C945,177.0 4415.0,177.0 4415.0,702.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-4\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">csubj</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M945,704.0 L937,692.0 953,692.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-5\" stroke-width=\"2px\" d=\"M1120,702.0 C1120,527.0 1420.0,527.0 1420.0,702.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-5\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">nummod</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M1120,704.0 L1112,692.0 1128,692.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-6\" stroke-width=\"2px\" d=\"M1295,702.0 C1295,614.5 1415.0,614.5 1415.0,702.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-6\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">advmod</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M1295,704.0 L1287,692.0 1303,692.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-7\" stroke-width=\"2px\" d=\"M945,702.0 C945,439.5 1425.0,439.5 1425.0,702.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-7\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">dobj</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M1425.0,704.0 L1433.0,692.0 1417.0,692.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-8\" stroke-width=\"2px\" d=\"M1470,702.0 C1470,614.5 1590.0,614.5 1590.0,702.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-8\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">prep</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M1590.0,704.0 L1598.0,692.0 1582.0,692.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-9\" stroke-width=\"2px\" d=\"M1820,702.0 C1820,527.0 2120.0,527.0 2120.0,702.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-9\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">amod</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M1820,704.0 L1812,692.0 1828,692.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-10\" stroke-width=\"2px\" d=\"M1995,702.0 C1995,614.5 2115.0,614.5 2115.0,702.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
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       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M1995,704.0 L1987,692.0 2003,692.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-11\" stroke-width=\"2px\" d=\"M1645,702.0 C1645,439.5 2125.0,439.5 2125.0,702.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
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       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M2125.0,704.0 L2133.0,692.0 2117.0,692.0\" fill=\"currentColor\"/>\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-12\" stroke-width=\"2px\" d=\"M945,702.0 C945,352.0 2305.0,352.0 2305.0,702.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
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       "        <textPath xlink:href=\"#arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-12\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">prep</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M2305.0,704.0 L2313.0,692.0 2297.0,692.0\" fill=\"currentColor\"/>\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-13\" stroke-width=\"2px\" d=\"M2520,702.0 C2520,527.0 2820.0,527.0 2820.0,702.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
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       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M2520,704.0 L2512,692.0 2528,692.0\" fill=\"currentColor\"/>\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-22\" stroke-width=\"2px\" d=\"M945,702.0 C945,264.5 4060.0,264.5 4060.0,702.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-22\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">npadvmod</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M4060.0,704.0 L4068.0,692.0 4052.0,692.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-23\" stroke-width=\"2px\" d=\"M4270,702.0 C4270,614.5 4390.0,614.5 4390.0,702.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-23\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">aux</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M4270,704.0 L4262,692.0 4278,692.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-24\" stroke-width=\"2px\" d=\"M595,702.0 C595,2.0 4425.0,2.0 4425.0,702.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-24\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">ccomp</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M4425.0,704.0 L4433.0,692.0 4417.0,692.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-25\" stroke-width=\"2px\" d=\"M4620,702.0 C4620,614.5 4740.0,614.5 4740.0,702.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-25\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">det</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M4620,704.0 L4612,692.0 4628,692.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-26\" stroke-width=\"2px\" d=\"M4445,702.0 C4445,527.0 4745.0,527.0 4745.0,702.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-26\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">dobj</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M4745.0,704.0 L4753.0,692.0 4737.0,692.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-27\" stroke-width=\"2px\" d=\"M4795,702.0 C4795,614.5 4915.0,614.5 4915.0,702.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-27\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">prep</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M4915.0,704.0 L4923.0,692.0 4907.0,692.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-28\" stroke-width=\"2px\" d=\"M4970,702.0 C4970,614.5 5090.0,614.5 5090.0,702.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-28\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">pobj</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M5090.0,704.0 L5098.0,692.0 5082.0,692.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-29\" stroke-width=\"2px\" d=\"M4445,702.0 C4445,439.5 5275.0,439.5 5275.0,702.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-29\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">prep</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M5275.0,704.0 L5283.0,692.0 5267.0,692.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "\n",
       "<g class=\"displacy-arrow\">\n",
       "    <path class=\"displacy-arc\" id=\"arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-30\" stroke-width=\"2px\" d=\"M5320,702.0 C5320,614.5 5440.0,614.5 5440.0,702.0\" fill=\"none\" stroke=\"currentColor\"/>\n",
       "    <text dy=\"1.25em\" style=\"font-size: 0.8em; letter-spacing: 1px\">\n",
       "        <textPath xlink:href=\"#arrow-bc286b48a4fe4e0291de71c084f2d0e3-0-30\" class=\"displacy-label\" startOffset=\"50%\" side=\"left\" fill=\"currentColor\" text-anchor=\"middle\">pobj</textPath>\n",
       "    </text>\n",
       "    <path class=\"displacy-arrowhead\" d=\"M5440.0,704.0 L5448.0,692.0 5432.0,692.0\" fill=\"currentColor\"/>\n",
       "</g>\n",
       "</svg></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for sent in doc.sents:\n",
    "    spacy.displacy.render(\n",
    "        sent,\n",
    "        style = \"ent\",\n",
    "        jupyter = True,\n",
    "    )\n",
    "\n",
    "    parse_svg: str = spacy.displacy.render(\n",
    "        sent,\n",
    "        style = \"dep\",\n",
    "        jupyter = True,\n",
    "    )\n",
    "\n",
    "    display(SVG(parse_svg))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "926b4ea1-d8f0-4562-b4f1-87d2887c1d43",
   "metadata": {},
   "source": [
    "## Layer 1: construct a lexical graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6268f061-c64d-42dc-a768-072ec9931e36",
   "metadata": {},
   "source": [
    "Scan the document tokens to add lemmas to the _textgraph_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9e87ac9e-effb-4184-94ef-8973f7ee8e27",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-12T20:28:19.506397Z",
     "iopub.status.busy": "2024-08-12T20:28:19.506201Z",
     "iopub.status.idle": "2024-08-12T20:28:20.136690Z",
     "shell.execute_reply": "2024-08-12T20:28:20.135679Z",
     "shell.execute_reply.started": "2024-08-12T20:28:19.506379Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ic| sent: Eating processed red meat could be a significant risk factor for dementia, according to a large study that tracked more than 100,000 people over four decades.\n",
      "ic| node_seq: [0, 1, 2, 3, 4, 5, 6]\n",
      "ic| sent: Processed red meat has previously been shown to increase the risk of cancer, heart disease and type 2 diabetes.\n",
      "ic| node_seq: [0, 1, 7, 8, 9, 10, 11]\n",
      "ic| sent: Now US researchers say they have uncovered a potential link to dementia.\n",
      "ic| node_seq: [12, 13, 14, 3]\n",
      "ic| sent: The study also found that replacing processed red meat with healthier foods such as nuts, beans or tofu could help reduce the risk of dementia.\n",
      "ic| node_seq: [4, 0, 15, 16, 17, 18, 1, 3]\n",
      "ic| sent: The findings were presented at the Alzheimer’s Association international conference in the US.\n",
      "ic| node_seq: [19, 20, 21, 22, 12]\n",
      "ic| sent: The number of people living with dementia globally is forecast to nearly triple to 153 million by 2050, and studies looking at"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 NOUN.meat meat\n",
      "8 NOUN.risk risk\n",
      "9 NOUN.factor factor\n",
      "11 NOUN.dementia dementia\n",
      "17 NOUN.study study\n",
      "23 NOUN.people people\n",
      "26 NOUN.decade decades\n",
      "31 NOUN.meat meat\n",
      "39 NOUN.risk risk\n",
      "41 NOUN.cancer cancer\n",
      "43 NOUN.heart heart\n",
      "44 NOUN.disease disease\n",
      "46 NOUN.type type\n",
      "48 NOUN.diabetes diabetes\n",
      "51 PROPN.us US\n",
      "52 NOUN.researcher researchers\n",
      "59 NOUN.link link\n",
      "61 NOUN.dementia dementia\n",
      "65 NOUN.study study\n",
      "72 NOUN.meat meat\n",
      "75 NOUN.food foods\n",
      "78 NOUN.nut nuts\n",
      "80 NOUN.bean beans\n",
      "82 NOUN.tofu tofu\n",
      "87 NOUN.risk risk\n",
      "89 NOUN.dementia dementia\n",
      "92 NOUN.finding findings\n",
      "97 PROPN.alzheimer Alzheimer\n",
      "99 PROPN.association Association\n",
      "101 NOUN.conference conference\n",
      "104 PROPN.us US\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " diet and risk of cognitive decline has become a focus of researchers.\n",
      "          \n",
      "ic| node_seq: [23, 5, 3, 4, 24, 1, 25, 26, 13]\n",
      "ic| sent: In the latest research, experts studied the health of 130,000 nurses and other health workers working in the US.\n",
      "ic| node_seq: [27, 28, 29, 30, 29, 31, 12]\n",
      "ic| sent: They were tracked for 43 years and provided data on their diet every 2 to 5 years.\n",
      "ic| node_seq: [32, 33, 24, 32]\n",
      "ic| sent: The participants were asked how often they ate processed red meat including bacon, hotdogs, sausages, salami and other sandwich meat.\n",
      "ic| node_seq: [34, 0, 35, 36, 37, 38, 39, 0]\n",
      "ic| sent: They were also asked about their consumption of nuts and legumes including peanut butter, peanuts, walnuts and other nuts, string beans, beans, peas, soy milk and tofu.\n",
      "          \n",
      "ic| node_seq: [40, 16, 41, 42, 43, 42, 44, 16, 45, 17, 17, 46, 47, 48, 18]\n",
      "ic"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "108 NOUN.number number\n",
      "110 NOUN.people people\n",
      "113 NOUN.dementia dementia\n",
      "127 NOUN.study studies\n",
      "130 NOUN.diet diet\n",
      "132 NOUN.risk risk\n",
      "135 NOUN.decline decline\n",
      "139 NOUN.focus focus\n",
      "141 NOUN.researcher researchers\n",
      "147 NOUN.research research\n",
      "149 NOUN.expert experts\n",
      "152 NOUN.health health\n",
      "155 NOUN.nurse nurses\n",
      "158 NOUN.health health\n",
      "159 NOUN.worker workers\n",
      "163 PROPN.us US\n",
      "170 NOUN.year years\n",
      "173 NOUN.datum data\n",
      "176 NOUN.diet diet\n",
      "181 NOUN.year years\n",
      "185 NOUN.participant participants\n",
      "194 NOUN.meat meat\n",
      "196 NOUN.bacon bacon\n",
      "198 NOUN.hotdog hotdogs\n",
      "200 NOUN.sausage sausages\n",
      "202 NOUN.salami salami\n",
      "205 NOUN.sandwich sandwich\n",
      "206 NOUN.meat meat\n",
      "215 NOUN.consumption consumption\n",
      "217 NOUN.nut nuts\n",
      "219 NOUN.legume legumes\n",
      "221 NOUN.peanut peanut\n",
      "222 NOUN.butter butter\n",
      "224 NOUN.peanut peanuts\n",
      "226 NOUN.walnut walnuts\n",
      "229 NOUN.nut nuts\n",
      "231 NOUN.string string\n",
      "232 NOUN.bean beans\n",
      "234 NOUN.bean beans\n",
      "236 NOUN.pea peas\n",
      "238 NOUN.soy soy\n",
      "239 NOUN.milk milk\n",
      "241 NOUN.tofu tofu\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "| sent: More than 11,000 cases of dementia were identified during the follow-up period.\n",
      "ic| node_seq: [49, 3, 50]\n",
      "ic| sent: Consuming two servings of processed red meat each week appeared to raise the risk of cognitive decline by 14% compared with those eating about three servings a month, the researchers reported.\n",
      "ic| node_seq: [51, 0, 52, 1, 25, 53, 51, 54, 13]\n",
      "ic| sent: The study also suggested that replacing one daily serving of processed red meat for a daily serving of nuts, beans or tofu every day could lower the risk of dementia by 23%.\n",
      "ic| node_seq: [4, 51, 0, 51, 16, 17, 18, 55, 1, 3, 53]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "247 NOUN.case cases\n",
      "249 NOUN.dementia dementia\n",
      "257 NOUN.period period\n",
      "262 NOUN.serving servings\n",
      "266 NOUN.meat meat\n",
      "268 NOUN.week week\n",
      "273 NOUN.risk risk\n",
      "276 NOUN.decline decline\n",
      "279 NOUN.% %\n",
      "286 NOUN.serving servings\n",
      "288 NOUN.month month\n",
      "291 NOUN.researcher researchers\n",
      "296 NOUN.study study\n",
      "303 NOUN.serving serving\n",
      "307 NOUN.meat meat\n",
      "311 NOUN.serving serving\n",
      "313 NOUN.nut nuts\n",
      "315 NOUN.bean beans\n",
      "317 NOUN.tofu tofu\n",
      "319 NOUN.day day\n",
      "323 NOUN.risk risk\n",
      "325 NOUN.dementia dementia\n",
      "328 NOUN.% %\n"
     ]
    }
   ],
   "source": [
    "for sent in doc.sents:\n",
    "    node_seq: typing.List[ int ] = []\n",
    "    ic(sent)\n",
    "\n",
    "    for tok in sent:\n",
    "        text: str = tok.text.strip()\n",
    "        \n",
    "        if tok.pos_ in [ \"NOUN\", \"PROPN\" ]:\n",
    "            key: str = tok.pos_ + \".\" + tok.lemma_.strip().lower()\n",
    "            print(tok.i, key, tok.text.strip())\n",
    "\n",
    "            if key not in known_lemma:\n",
    "                # create a new node\n",
    "                known_lemma.append(key)\n",
    "                node_id: int = known_lemma.index(key)\n",
    "                node_seq.append(node_id)\n",
    "\n",
    "                graph.add_node(\n",
    "                    node_id,\n",
    "                    key = key,\n",
    "                    kind = \"Lemma\",\n",
    "                    pos = tok.pos_,\n",
    "                    text = text,\n",
    "                    chunk = chunk.uid,\n",
    "                    count = 1,\n",
    "                )\n",
    "            else:\n",
    "                # link to an existing node, adding weight\n",
    "                node_id = known_lemma.index(key)\n",
    "                node_seq.append(node_id)\n",
    "\n",
    "                node: dict = graph.nodes[node_id]\n",
    "                node[\"count\"] += 1\n",
    "\n",
    "    # create the textrank edges\n",
    "    ic(node_seq)\n",
    "\n",
    "    for hop in range(TR_LOOKBACK):\n",
    "        for node_id, node in enumerate(node_seq[: -1 - hop]):            \n",
    "            neighbor: int = node_seq[hop + node_id + 1]\n",
    "            graph.add_edge(\n",
    "                node,\n",
    "                neighbor,\n",
    "                rel = \"FOLLOWS_LEXICALLY\",\n",
    "            )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a676db9a-142e-42b9-916c-853a0f75ae0a",
   "metadata": {},
   "source": [
    "Keep track of the sentence numbers, which we'll use later for entity co-occurrence links."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f7eaa2e9-83ca-4e15-a38a-ce3659c373b8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-12T20:28:20.137897Z",
     "iopub.status.busy": "2024-08-12T20:28:20.137774Z",
     "iopub.status.idle": "2024-08-12T20:28:20.140871Z",
     "shell.execute_reply": "2024-08-12T20:28:20.140374Z",
     "shell.execute_reply.started": "2024-08-12T20:28:20.137884Z"
    }
   },
   "outputs": [],
   "source": [
    "sent_map: typing.Dict[ spacy.tokens.span.Span, int ] = {}\n",
    "\n",
    "for sent_id, sent in enumerate(doc.sents):\n",
    "    sent_map[sent] = sent_id"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba51a1f7-517b-4bf9-8153-c41cd15b952d",
   "metadata": {},
   "source": [
    "## Layer 2: overlay entities onto the graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b8bc6b8-332c-409b-80d4-9f9f7bc9286e",
   "metadata": {},
   "source": [
    "Classify spans as potential entities.\n",
    "\n",
    "Note that if we'd run [_entity resolution_](https://neo4j.com/developer-blog/entity-resolved-knowledge-graphs/) previously from _structured_ or _semi-structured_ data sources to generate a \"backbone\" for the knowledge graph, then we use the contextualized _surface forms_ from that phase to perform _entity linking_ on the entities extracted here from _unstructured_ data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7c5b8720-3a08-4e52-9911-959c5e34b956",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-12T20:28:20.141597Z",
     "iopub.status.busy": "2024-08-12T20:28:20.141493Z",
     "iopub.status.idle": "2024-08-12T20:28:20.146243Z",
     "shell.execute_reply": "2024-08-12T20:28:20.145805Z",
     "shell.execute_reply.started": "2024-08-12T20:28:20.141583Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass(order=False, frozen=False)\n",
    "class Entity:\n",
    "    loc: typing.Tuple[ int ]\n",
    "    key: str\n",
    "    text: str\n",
    "    label: str\n",
    "    chunk_id: int\n",
    "    sent_id: int\n",
    "    span: spacy.tokens.span.Span\n",
    "    node: typing.Optional[ int ] = None\n",
    "\n",
    "\n",
    "span_decoder: typing.Dict[ tuple, Entity ] = {}\n",
    "\n",
    "\n",
    "def make_entity (\n",
    "    span: spacy.tokens.span.Span,\n",
    "    chunk: TextChunk,\n",
    "    ) -> Entity:\n",
    "    \"\"\"\n",
    "Instantiate one `Entity` dataclass object, adding it to the working \"vocabulary\".\n",
    "    \"\"\"\n",
    "    key: str = \" \".join([\n",
    "        tok.pos_ + \".\" + tok.lemma_.strip().lower()\n",
    "        for tok in span\n",
    "    ])\n",
    "    \n",
    "    ent: Entity = Entity(\n",
    "        ( span.start, span.end, ),\n",
    "        key,\n",
    "        span.text,\n",
    "        span.label_,\n",
    "        chunk.uid,\n",
    "        sent_map[span.sent],\n",
    "        span,\n",
    "    )\n",
    "\n",
    "    if ent.loc not in span_decoder:\n",
    "        span_decoder[ent.loc] = ent\n",
    "        ic(ent)\n",
    "\n",
    "    return ent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "332266a3-19d2-4cd2-adfd-5c04b55539db",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-12T20:28:20.147285Z",
     "iopub.status.busy": "2024-08-12T20:28:20.147152Z",
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    },
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    {
     "name": "stderr",
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     "text": [
      "ic| ent: Entity(loc=(1, 4),\n",
      "                key='VERB.process ADJ.red NOUN.meat',\n",
      "                text='processed red meat',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=0,\n",
      "                span=processed red meat,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(11, 12),\n",
      "                key='NOUN.dementia',\n",
      "                text='dementia',\n",
      "                label='Condition',\n",
      "                chunk_id=1,\n",
      "                sent_id=0,\n",
      "                span=dementia,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(29, 32),\n",
      "                key='ADJ.processed ADJ.red NOUN.meat',\n",
      "                text='Processed red meat',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=1,\n",
      "                span=Processed red meat,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(41, 42),\n",
      "                key='NOUN.cancer',\n",
      "                text='cancer',\n",
      "                label='Condition',\n",
      "                chunk_id=1,\n",
      "                sent_id=1,\n",
      "                span=cancer,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(43, 45),\n",
      "                key='NOUN.heart NOUN.disease',\n",
      "                text='heart disease',\n",
      "                label='Condition',\n",
      "                chunk_id=1,\n",
      "                sent_id=1,\n",
      "                span=heart disease,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(46, 49),\n",
      "                key='NOUN.type NUM.2 NOUN.diabetes',\n",
      "                text='type 2 diabetes',\n",
      "                label='Condition',\n",
      "                chunk_id=1,\n",
      "                sent_id=1,\n",
      "                span=type 2 diabetes,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(51, 52),\n",
      "                key='PROPN.us',\n",
      "                text='US',\n",
      "                label='Country',\n",
      "                chunk_id=1,\n",
      "                sent_id=2,\n",
      "                span=US,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(61, 62),\n",
      "                key='NOUN.dementia',\n",
      "                text='dementia',\n",
      "                label='Condition',\n",
      "                chunk_id=1,\n",
      "                sent_id=2,\n",
      "                span=dementia,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(70, 73),\n",
      "                key='VERB.process ADJ.red NOUN.meat',\n",
      "                text='processed red meat',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=3,\n",
      "                span=processed red meat,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(78, 79),\n",
      "                key='NOUN.nut',\n",
      "                text='nuts',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=3,\n",
      "                span=nuts,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(80, 81),\n",
      "                key='NOUN.bean',\n",
      "                text='beans',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=3,\n",
      "                span=beans,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(82, 83),\n",
      "                key='NOUN.tofu',\n",
      "                text='tofu',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=3,\n",
      "                span=tofu,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(89, 90),\n",
      "                key='NOUN.dementia',\n",
      "                text='dementia',\n",
      "                label='Condition',\n",
      "                chunk_id=1,\n",
      "                sent_id=3,\n",
      "                span=dementia,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(97, 102),\n",
      "                key='PROPN.alzheimer PART.’s PROPN.association ADJ.international '\n",
      "                    'NOUN.conference',\n",
      "                text='Alzheimer’s Association international conference',\n",
      "                label='Conference',\n",
      "                chunk_id=1,\n",
      "                sent_id=4,\n",
      "                span=Alzheimer’s Association international conference,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(104, 105),\n",
      "                key='PROPN.us',\n",
      "                text='US',\n",
      "                label='Country',\n",
      "                chunk_id=1,\n",
      "                sent_id=4,\n",
      "                span=US,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(113, 114),\n",
      "                key='NOUN.dementia',\n",
      "                text='dementia',\n",
      "                label='Condition',\n",
      "                chunk_id=1,\n",
      "                sent_id=5,\n",
      "                span=dementia,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(134, 136),\n",
      "                key='ADJ.cognitive NOUN.decline',\n",
      "                text='cognitive decline',\n",
      "                label='Condition',\n",
      "                chunk_id=1,\n",
      "                sent_id=5,\n",
      "                span=cognitive decline,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(141, 142),\n",
      "                key='NOUN.researcher',\n",
      "                text='researchers',\n",
      "                label='People Group',\n",
      "                chunk_id=1,\n",
      "                sent_id=5,\n",
      "                span=researchers,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(163, 164),\n",
      "                key='PROPN.us',\n",
      "                text='US',\n",
      "                label='Country',\n",
      "                chunk_id=1,\n",
      "                sent_id=6,\n",
      "                span=US,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(193, 195),\n",
      "                key='ADJ.red NOUN.meat',\n",
      "                text='red meat',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=8,\n",
      "                span=red meat,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(196, 197),\n",
      "                key='NOUN.bacon',\n",
      "                text='bacon',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=8,\n",
      "                span=bacon,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(198, 199),\n",
      "                key='NOUN.hotdog',\n",
      "                text='hotdogs',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=8,\n",
      "                span=hotdogs,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(200, 201),\n",
      "                key='NOUN.sausage',\n",
      "                text='sausages',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=8,\n",
      "                span=sausages,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(202, 203),\n",
      "                key='NOUN.salami',\n",
      "                text='salami',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=8,\n",
      "                span=salami,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(221, 223),\n",
      "                key='NOUN.peanut NOUN.butter',\n",
      "                text='peanut butter',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=9,\n",
      "                span=peanut butter,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(224, 225),\n",
      "                key='NOUN.peanut',\n",
      "                text='peanuts',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=9,\n",
      "                span=peanuts,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(226, 227),\n",
      "                key='NOUN.walnut',\n",
      "                text='walnuts',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=9,\n",
      "                span=walnuts,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(229, 230),\n",
      "                key='NOUN.nut',\n",
      "                text='nuts',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=9,\n",
      "                span=nuts,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(231, 233),\n",
      "                key='NOUN.string NOUN.bean',\n",
      "                text='string beans',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=9,\n",
      "                span=string beans,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(234, 235),\n",
      "                key='NOUN.bean',\n",
      "                text='beans',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=9,\n",
      "                span=beans,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(236, 237),\n",
      "                key='NOUN.pea',\n",
      "                text='peas',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=9,\n",
      "                span=peas,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(238, 240),\n",
      "                key='NOUN.soy NOUN.milk',\n",
      "                text='soy milk',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=9,\n",
      "                span=soy milk,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(241, 242),\n",
      "                key='NOUN.tofu',\n",
      "                text='tofu',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=9,\n",
      "                span=tofu,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(249, 250),\n",
      "                key='NOUN.dementia',\n",
      "                text='dementia',\n",
      "                label='Condition',\n",
      "                chunk_id=1,\n",
      "                sent_id=10,\n",
      "                span=dementia,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(265, 267),\n",
      "                key='ADJ.red NOUN.meat',\n",
      "                text='red meat',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=11,\n",
      "                span=red meat,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(275, 277),\n",
      "                key='ADJ.cognitive NOUN.decline',\n",
      "                text='cognitive decline',\n",
      "                label='Condition',\n",
      "                chunk_id=1,\n",
      "                sent_id=11,\n",
      "                span=cognitive decline,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(296, 297),\n",
      "                key='NOUN.study',\n",
      "                text='study',\n",
      "                label='Research',\n",
      "                chunk_id=1,\n",
      "                sent_id=12,\n",
      "                span=study,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(306, 308),\n",
      "                key='ADJ.red NOUN.meat',\n",
      "                text='red meat',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=12,\n",
      "                span=red meat,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(313, 314),\n",
      "                key='NOUN.nut',\n",
      "                text='nuts',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=12,\n",
      "                span=nuts,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(315, 316),\n",
      "                key='NOUN.bean',\n",
      "                text='beans',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=12,\n",
      "                span=beans,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(317, 318),\n",
      "                key='NOUN.tofu',\n",
      "                text='tofu',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=12,\n",
      "                span=tofu,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(325, 326),\n",
      "                key='NOUN.dementia',\n",
      "                text='dementia',\n",
      "                label='Condition',\n",
      "                chunk_id=1,\n",
      "                sent_id=12,\n",
      "                span=dementia,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(6, 10),\n",
      "                key='DET.a ADJ.significant NOUN.risk NOUN.factor',\n",
      "                text='a significant risk factor',\n",
      "                label='NP',\n",
      "                chunk_id=1,\n",
      "                sent_id=0,\n",
      "                span=a significant risk factor,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(15, 18),\n",
      "                key='DET.a ADJ.large NOUN.study',\n",
      "                text='a large study',\n",
      "                label='NP',\n",
      "                chunk_id=1,\n",
      "                sent_id=0,\n",
      "                span=a large study,\n",
      "                node=None)\n",
      "ic| ent: Entity(loc=(18, 19),\n",
      "                key='PRON.that',\n",
      "                text='that',\n",
      "                label='NPIOPub message rate exceeded.\n",
      "The Jupyter server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--ServerApp.iopub_msg_rate_limit`.\n",
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      "Current values:\n",
      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for span in doc.ents:\n",
    "    make_entity(span, chunk)\n",
    "\n",
    "for span in doc.noun_chunks:\n",
    "    make_entity(span, chunk)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22be16df-58d3-4355-bf0f-46365a2ca387",
   "metadata": {},
   "source": [
    "Overlay the inferred entity spans atop the base layer constructed by _textgraph_ analysis of the `spaCy` parse trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d1a525c1-7f62-401a-ab91-81ef13f325a1",
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    "execution": {
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   "source": [
    "def extract_entity (\n",
    "    ent: Entity,\n",
    "    ) -> None:\n",
    "    \"\"\"\n",
    "Link one `Entity` into the existing graph.\n",
    "    \"\"\"\n",
    "    if ent.key not in known_lemma:\n",
    "        # add a new Entity node to the graph and link to its component Lemma nodes\n",
    "        known_lemma.append(ent.key)\n",
    "        node_id: int = known_lemma.index(ent.key)\n",
    "        \n",
    "        graph.add_node(\n",
    "            node_id,\n",
    "            key = ent.key,\n",
    "            kind = \"Entity\",\n",
    "            label = ent.label,\n",
    "            pos = \"NP\",\n",
    "            text = ent.text,\n",
    "            chunk = ent.chunk_id,\n",
    "            count = 1,\n",
    "        )\n",
    "\n",
    "        for tok in ent.span:\n",
    "            tok_key: str = tok.pos_ + \".\" + tok.lemma_.strip().lower()\n",
    "\n",
    "            if tok_key in known_lemma:\n",
    "                tok_idx: int = known_lemma.index(tok_key)\n",
    "\n",
    "                graph.add_edge(\n",
    "                    node_id,\n",
    "                    tok_idx,\n",
    "                    rel = \"COMPOUND_ELEMENT_OF\",\n",
    "                )\n",
    "    else:\n",
    "        node_id: int = known_lemma.index(ent.key)\n",
    "        node: dict = graph.nodes[node_id]\n",
    "        # promote to an Entity, in case the node had been a Lemma\n",
    "        node[\"kind\"] = \"Entity\"\n",
    "        node[\"chunk\"] = ent.chunk_id\n",
    "        node[\"count\"] += 1\n",
    "\n",
    "        # select the more specific label\n",
    "        if \"label\" not in node or node[\"label\"] == \"NP\":\n",
    "          node[\"label\"] = ent.label\n",
    "\n",
    "    ent.node = node_id"
   ]
  },
  {
   "cell_type": "code",
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   "id": "6ac35293-8fe1-455a-9ab1-27f9302f1bb2",
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ic| ent: Entity(loc=(1, 4),\n",
      "                key='VERB.process ADJ.red NOUN.meat',\n",
      "                text='processed red meat',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=0,\n",
      "                span=processed red meat,\n",
      "                node=56)\n",
      "ic| ent: Entity(loc=(11, 12),\n",
      "                key='NOUN.dementia',\n",
      "                text='dementia',\n",
      "                label='Condition',\n",
      "                chunk_id=1,\n",
      "                sent_id=0,\n",
      "                span=dementia,\n",
      "                node=3)\n",
      "ic| ent: Entity(loc=(29, 32),\n",
      "                key='ADJ.processed ADJ.red NOUN.meat',\n",
      "                text='Processed red meat',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=1,\n",
      "                span=Processed red meat,\n",
      "                node=57)\n",
      "ic| ent: Entity(loc=(41, 42),\n",
      "                key='NOUN.cancer',\n",
      "                text='cancer',\n",
      "                label='Condition',\n",
      "                chunk_id=1,\n",
      "                sent_id=1,\n",
      "                span=cancer,\n",
      "                node=7)\n",
      "ic| ent: Entity(loc=(43, 45),\n",
      "                key='NOUN.heart NOUN.disease',\n",
      "                text='heart disease',\n",
      "                label='Condition',\n",
      "                chunk_id=1,\n",
      "                sent_id=1,\n",
      "                span=heart disease,\n",
      "                node=58)\n",
      "ic| ent: Entity(loc=(46, 49),\n",
      "                key='NOUN.type NUM.2 NOUN.diabetes',\n",
      "                text='type 2 diabetes',\n",
      "                label='Condition',\n",
      "                chunk_id=1,\n",
      "                sent_id=1,\n",
      "                span=type 2 diabetes,\n",
      "                node=59)\n",
      "ic| ent: Entity(loc=(51, 52),\n",
      "                key='PROPN.us',\n",
      "                text='US',\n",
      "                label='Country',\n",
      "                chunk_id=1,\n",
      "                sent_id=2,\n",
      "                span=US,\n",
      "                node=12)\n",
      "ic| ent: Entity(loc=(61, 62),\n",
      "                key='NOUN.dementia',\n",
      "                text='dementia',\n",
      "                label='Condition',\n",
      "                chunk_id=1,\n",
      "                sent_id=2,\n",
      "                span=dementia,\n",
      "                node=3)\n",
      "ic| ent: Entity(loc=(70, 73),\n",
      "                key='VERB.process ADJ.red NOUN.meat',\n",
      "                text='processed red meat',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=3,\n",
      "                span=processed red meat,\n",
      "                node=56)\n",
      "ic| ent: Entity(loc=(78, 79),\n",
      "                key='NOUN.nut',\n",
      "                text='nuts',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=3,\n",
      "                span=nuts,\n",
      "                node=16)\n",
      "ic| ent: Entity(loc=(80, 81),\n",
      "                key='NOUN.bean',\n",
      "                text='beans',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=3,\n",
      "                span=beans,\n",
      "                node=17)\n",
      "ic| ent: Entity(loc=(82, 83),\n",
      "                key='NOUN.tofu',\n",
      "                text='tofu',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=3,\n",
      "                span=tofu,\n",
      "                node=18)\n",
      "ic| ent: Entity(loc=(89, 90),\n",
      "                key='NOUN.dementia',\n",
      "                text='dementia',\n",
      "                label='Condition',\n",
      "                chunk_id=1,\n",
      "                sent_id=3,\n",
      "                span=dementia,\n",
      "                node=3)\n",
      "ic| ent: Entity(loc=(97, 102),\n",
      "                key='PROPN.alzheimer PART.’s PROPN.association ADJ.international '\n",
      "                    'NOUN.conference',\n",
      "                text='Alzheimer’s Association international conference',\n",
      "                label='Conference',\n",
      "                chunk_id=1,\n",
      "                sent_id=4,\n",
      "                span=Alzheimer’s Association international conference,\n",
      "                node=60)\n",
      "ic| ent: Entity(loc=(104, 105),\n",
      "                key='PROPN.us',\n",
      "                text='US',\n",
      "                label='Country',\n",
      "                chunk_id=1,\n",
      "                sent_id=4,\n",
      "                span=US,\n",
      "                node=12)\n",
      "ic| ent: Entity(loc=(113, 114),\n",
      "                key='NOUN.dementia',\n",
      "                text='dementia',\n",
      "                label='Condition',\n",
      "                chunk_id=1,\n",
      "                sent_id=5,\n",
      "                span=dementia,\n",
      "                node=3)\n",
      "ic| ent: Entity(loc=(134, 136),\n",
      "                key='ADJ.cognitive NOUN.decline',\n",
      "                text='cognitive decline',\n",
      "                label='Condition',\n",
      "                chunk_id=1,\n",
      "                sent_id=5,\n",
      "                span=cognitive decline,\n",
      "                node=61)\n",
      "ic| ent: Entity(loc=(141, 142),\n",
      "                key='NOUN.researcher',\n",
      "                text='researchers',\n",
      "                label='People Group',\n",
      "                chunk_id=1,\n",
      "                sent_id=5,\n",
      "                span=researchers,\n",
      "                node=13)\n",
      "ic| ent: Entity(loc=(163, 164),\n",
      "                key='PROPN.us',\n",
      "                text='US',\n",
      "                label='Country',\n",
      "                chunk_id=1,\n",
      "                sent_id=6,\n",
      "                span=US,\n",
      "                node=12)\n",
      "ic| ent: Entity(loc=(193, 195),\n",
      "                key='ADJ.red NOUN.meat',\n",
      "                text='red meat',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=8,\n",
      "                span=red meat,\n",
      "                node=62)\n",
      "ic| ent: Entity(loc=(196, 197),\n",
      "                key='NOUN.bacon',\n",
      "                text='bacon',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=8,\n",
      "                span=bacon,\n",
      "                node=35)\n",
      "ic| ent: Entity(loc=(198, 199),\n",
      "                key='NOUN.hotdog',\n",
      "                text='hotdogs',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=8,\n",
      "                span=hotdogs,\n",
      "                node=36)\n",
      "ic| ent: Entity(loc=(200, 201),\n",
      "                key='NOUN.sausage',\n",
      "                text='sausages',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=8,\n",
      "                span=sausages,\n",
      "                node=37)\n",
      "ic| ent: Entity(loc=(202, 203),\n",
      "                key='NOUN.salami',\n",
      "                text='salami',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=8,\n",
      "                span=salami,\n",
      "                node=38)\n",
      "ic| ent: Entity(loc=(221, 223),\n",
      "                key='NOUN.peanut NOUN.butter',\n",
      "                text='peanut butter',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=9,\n",
      "                span=peanut butter,\n",
      "                node=63)\n",
      "ic| ent: Entity(loc=(224, 225),\n",
      "                key='NOUN.peanut',\n",
      "                text='peanuts',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=9,\n",
      "                span=peanuts,\n",
      "                node=42)\n",
      "ic| ent: Entity(loc=(226, 227),\n",
      "                key='NOUN.walnut',\n",
      "                text='walnuts',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=9,\n",
      "                span=walnuts,\n",
      "                node=44)\n",
      "ic| ent: Entity(loc=(229, 230),\n",
      "                key='NOUN.nut',\n",
      "                text='nuts',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=9,\n",
      "                span=nuts,\n",
      "                node=16)\n",
      "ic| ent: Entity(loc=(231, 233),\n",
      "                key='NOUN.string NOUN.bean',\n",
      "                text='string beans',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=9,\n",
      "                span=string beans,\n",
      "                node=64)\n",
      "ic| ent: Entity(loc=(234, 235),\n",
      "                key='NOUN.bean',\n",
      "                text='beans',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=9,\n",
      "                span=beans,\n",
      "                node=17)\n",
      "ic| ent: Entity(loc=(236, 237),\n",
      "                key='NOUN.pea',\n",
      "                text='peas',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=9,\n",
      "                span=peas,\n",
      "                node=46)\n",
      "ic| ent: Entity(loc=(238, 240),\n",
      "                key='NOUN.soy NOUN.milk',\n",
      "                text='soy milk',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=9,\n",
      "                span=soy milk,\n",
      "                node=65)\n",
      "ic| ent: Entity(loc=(241, 242),\n",
      "                key='NOUN.tofu',\n",
      "                text='tofu',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=9,\n",
      "                span=tofu,\n",
      "                node=18)\n",
      "ic| ent: Entity(loc=(249, 250),\n",
      "                key='NOUN.dementia',\n",
      "                text='dementia',\n",
      "                label='Condition',\n",
      "                chunk_id=1,\n",
      "                sent_id=10,\n",
      "                span=dementia,\n",
      "                node=3)\n",
      "ic| ent: Entity(loc=(265, 267),\n",
      "                key='ADJ.red NOUN.meat',\n",
      "                text='red meat',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=11,\n",
      "                span=red meat,\n",
      "                node=62)\n",
      "ic| ent: Entity(loc=(275, 277),\n",
      "                key='ADJ.cognitive NOUN.decline',\n",
      "                text='cognitive decline',\n",
      "                label='Condition',\n",
      "                chunk_id=1,\n",
      "                sent_id=11,\n",
      "                span=cognitive decline,\n",
      "                node=61)\n",
      "ic| ent: Entity(loc=(296, 297),\n",
      "                key='NOUN.study',\n",
      "                text='study',\n",
      "                label='Research',\n",
      "                chunk_id=1,\n",
      "                sent_id=12,\n",
      "                span=study,\n",
      "                node=4)\n",
      "ic| ent: Entity(loc=(306, 308),\n",
      "                key='ADJ.red NOUN.meat',\n",
      "                text='red meat',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=12,\n",
      "                span=red meat,\n",
      "                node=62)\n",
      "ic| ent: Entity(loc=(313, 314),\n",
      "                key='NOUN.nut',\n",
      "                text='nuts',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=12,\n",
      "                span=nuts,\n",
      "                node=16)\n",
      "ic| ent: Entity(loc=(315, 316),\n",
      "                key='NOUN.bean',\n",
      "                text='beans',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=12,\n",
      "                span=beans,\n",
      "                node=17)\n",
      "ic| ent: Entity(loc=(317, 318),\n",
      "                key='NOUN.tofu',\n",
      "                text='tofu',\n",
      "                label='Food',\n",
      "                chunk_id=1,\n",
      "                sent_id=12,\n",
      "                span=tofu,\n",
      "                node=18)\n",
      "ic| ent: Entity(loc=(325, 326),\n",
      "                key='NOUN.dementia',\n",
      "                text='dementia',\n",
      "                label='Condition',\n",
      "                chunk_id=1,\n",
      "                sent_id=12,\n",
      "                span=dementia,\n",
      "                node=3)\n",
      "ic| ent: Entity(loc=(6, 10),\n",
      "                key='DET.a ADJ.significant NOUN.risk NOUN.factor',\n",
      "                text='a significant risk factor',\n",
      "                label='NP',\n",
      "                chunk_id=1,\n",
      "                sent_id=0,\n",
      "                span=a significant risk factor,\n",
      "                node=66)\n",
      "ic| ent: Entity(loc=(15, 18),\n",
      "                key='DET.a ADJ.large NOUN.study',\n",
      "                text='a large study',\n",
      "                label='NP',\n",
      "                chunk_id=1,\n",
      "                sent_id=0,\n",
      "                span=a large study,\n",
      "                node=67)\n",
      "ic| ent: Entity(loc=(20, 24),\n",
      "                key='ADJ.more ADP.than NUM.100,000 NOUN.people',\n",
      "                text='more than 100,000 people',\n",
      "                label='NPIOPub message rate exceeded.\n",
      "The Jupyter server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--ServerApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for ent in span_decoder.values():\n",
    "    if ent.key not in STOP_WORDS:\n",
    "        extract_entity(ent)\n",
    "        ic(ent)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3055708e-23b3-42fd-8fe6-30d40d0eccd5",
   "metadata": {},
   "source": [
    "Report the relations inferred by `GLiREL`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4eaee19b-91b5-4eca-bdcc-15d58c39a975",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-12T20:28:26.425296Z",
     "iopub.status.busy": "2024-08-12T20:28:26.425167Z",
     "iopub.status.idle": "2024-08-12T20:28:26.453010Z",
     "shell.execute_reply": "2024-08-12T20:28:26.452613Z",
     "shell.execute_reply.started": "2024-08-12T20:28:26.425284Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ic| ent: Entity(loc=(96, 100),\n",
      "                key='DET.the PROPN.alzheimer PART.’s PROPN.association',\n",
      "                text='the Alzheimer’s Association',\n",
      "                label='',\n",
      "                chunk_id=1,\n",
      "                sent_id=4,\n",
      "                span=the Alzheimer’s Association,\n",
      "                node=None)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MISSING src entity: ['the', 'Alzheimer', '’s', 'Association'] [96, 100]\n",
      "the Alzheimer’s Association 101 -> COUNTRY_OF_ORIGIN -> US 12 | 0.263\n",
      "the Alzheimer’s Association 101 -> COUNTRY_OF_ORIGIN -> US 12 | 0.258\n",
      "the Alzheimer’s Association 101 -> COUNTRY_OF_ORIGIN -> US 12 | 0.248\n"
     ]
    }
   ],
   "source": [
    "relations: typing.List[ dict ] = sorted(\n",
    "    doc._.relations,\n",
    "    key = lambda x: x[\"score\"],\n",
    "    reverse = True,\n",
    ")\n",
    "\n",
    "for item in relations:\n",
    "    src_loc: typing.Tuple[ int ] = tuple(item[\"head_pos\"])\n",
    "    dst_loc: typing.Tuple[ int ] = tuple(item[\"tail_pos\"])\n",
    "    skip_rel: bool = False\n",
    "\n",
    "    if src_loc not in span_decoder:\n",
    "        print(\"MISSING src entity:\", item[\"head_text\"], item[\"head_pos\"])\n",
    "        \n",
    "        src_ent: Entity = make_entity(\n",
    "            doc[ item[\"head_pos\"][0] : item[\"head_pos\"][1] ],\n",
    "            chunk,\n",
    "        )\n",
    "\n",
    "        if src_ent.key in STOP_WORDS:\n",
    "            skip_rel = True\n",
    "        else:\n",
    "            extract_entity(src_ent)\n",
    "\n",
    "    if dst_loc not in span_decoder:\n",
    "        print(\"MISSING dst entity:\", item[\"tail_text\"], item[\"tail_pos\"])\n",
    "\n",
    "        dst_ent: Entity = make_entity(\n",
    "            doc[ item[\"tail_pos\"][0] : item[\"tail_pos\"][1] ],\n",
    "            chunk,\n",
    "        )\n",
    "\n",
    "        if dst_ent.key in STOP_WORDS:\n",
    "            skip_rel = True\n",
    "        else:\n",
    "            extract_entity(dst_ent)\n",
    "\n",
    "    # link the connected nodes\n",
    "    if not skip_rel:\n",
    "        src_ent = span_decoder[src_loc]\n",
    "        dst_ent = span_decoder[dst_loc]\n",
    "\n",
    "        rel: str = item[\"label\"].strip().replace(\" \", \"_\").upper()\n",
    "        prob: float = round(item[\"score\"], 3)\n",
    "\n",
    "        print(f\"{src_ent.text} {src_ent.node} -> {rel} -> {dst_ent.text} {dst_ent.node} | {prob}\")\n",
    "\n",
    "        graph.add_edge(\n",
    "            src_ent.node,\n",
    "            dst_ent.node,\n",
    "            rel = rel,\n",
    "            prob = prob,\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f305abab-f875-4b74-80ce-36f9619d3e91",
   "metadata": {},
   "source": [
    "Connect the co-occurring entities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8f3ffd6e-ff44-456f-88ec-560d17a646d9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-12T20:28:26.453745Z",
     "iopub.status.busy": "2024-08-12T20:28:26.453607Z",
     "iopub.status.idle": "2024-08-12T20:28:26.457449Z",
     "shell.execute_reply": "2024-08-12T20:28:26.457036Z",
     "shell.execute_reply.started": "2024-08-12T20:28:26.453731Z"
    }
   },
   "outputs": [],
   "source": [
    "ent_map: typing.Dict[ int, typing.Set[ int ]] = defaultdict(set)\n",
    "\n",
    "for ent in span_decoder.values():\n",
    "    if ent.node is not None:\n",
    "        ent_map[ent.sent_id].add(ent.node)    \n",
    "\n",
    "for sent_id, nodes in ent_map.items():\n",
    "    for pair in itertools.combinations(list(nodes), 2):\n",
    "        if not graph.has_edge(*pair):\n",
    "            graph.add_edge(\n",
    "                pair[0],\n",
    "                pair[1],\n",
    "                rel = \"CO_OCCURS_WITH\",\n",
    "                prob = 1.0,\n",
    "            )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef531f80-7ccb-4569-8119-d59e196435f1",
   "metadata": {},
   "source": [
    "Run eigenvalue centrality (i.e., _Personalized PageRank_) to rank the entities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e15e1df8-3895-481e-b5e5-1d4148159b28",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-12T20:28:26.458261Z",
     "iopub.status.busy": "2024-08-12T20:28:26.458144Z",
     "iopub.status.idle": "2024-08-12T20:28:26.463798Z",
     "shell.execute_reply": "2024-08-12T20:28:26.463516Z",
     "shell.execute_reply.started": "2024-08-12T20:28:26.458247Z"
    }
   },
   "outputs": [],
   "source": [
    "for node, rank in nx.pagerank(graph, alpha = TR_ALPHA, weight = \"count\").items():\n",
    "    graph.nodes[node][\"rank\"] = rank"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0859353-9b9c-4a29-b7b6-190600751547",
   "metadata": {},
   "source": [
    "Report the top-ranked entities extracted from this text chunk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8e0bd4df-53f0-4818-ac3f-88e7d24e031c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-12T20:28:26.464470Z",
     "iopub.status.busy": "2024-08-12T20:28:26.464362Z",
     "iopub.status.idle": "2024-08-12T20:28:26.475476Z",
     "shell.execute_reply": "2024-08-12T20:28:26.475218Z",
     "shell.execute_reply.started": "2024-08-12T20:28:26.464456Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>kind</th>\n",
       "      <th>pos</th>\n",
       "      <th>text</th>\n",
       "      <th>chunk</th>\n",
       "      <th>count</th>\n",
       "      <th>label</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NOUN.dementia</td>\n",
       "      <td>Entity</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>dementia</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>Condition</td>\n",
       "      <td>0.034907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PROPN.us</td>\n",
       "      <td>Entity</td>\n",
       "      <td>PROPN</td>\n",
       "      <td>US</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Country</td>\n",
       "      <td>0.024389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NOUN.bean</td>\n",
       "      <td>Entity</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>beans</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>Food</td>\n",
       "      <td>0.023435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>VERB.process ADJ.red NOUN.meat</td>\n",
       "      <td>Entity</td>\n",
       "      <td>NP</td>\n",
       "      <td>processed red meat</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Food</td>\n",
       "      <td>0.023368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NOUN.nut</td>\n",
       "      <td>Entity</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>nuts</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>Food</td>\n",
       "      <td>0.022787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NOUN.study</td>\n",
       "      <td>Entity</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>study</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Research</td>\n",
       "      <td>0.021828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NOUN.tofu</td>\n",
       "      <td>Entity</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>tofu</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Food</td>\n",
       "      <td>0.021223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NOUN.risk</td>\n",
       "      <td>Entity</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>risk</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>NP</td>\n",
       "      <td>0.021021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>DET.the NOUN.risk</td>\n",
       "      <td>Entity</td>\n",
       "      <td>NP</td>\n",
       "      <td>the risk</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>NP</td>\n",
       "      <td>0.020222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ADJ.red NOUN.meat</td>\n",
       "      <td>Entity</td>\n",
       "      <td>NP</td>\n",
       "      <td>red meat</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Food</td>\n",
       "      <td>0.018906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NOUN.researcher</td>\n",
       "      <td>Entity</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>researchers</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>People Group</td>\n",
       "      <td>0.016184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ADJ.cognitive NOUN.decline</td>\n",
       "      <td>Entity</td>\n",
       "      <td>NP</td>\n",
       "      <td>cognitive decline</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Condition</td>\n",
       "      <td>0.014067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NOUN.diet</td>\n",
       "      <td>Entity</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>diet</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NP</td>\n",
       "      <td>0.013865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>DET.the PROPN.us</td>\n",
       "      <td>Entity</td>\n",
       "      <td>NP</td>\n",
       "      <td>the US</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NP</td>\n",
       "      <td>0.012369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NOUN.peanut</td>\n",
       "      <td>Entity</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>peanut</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Food</td>\n",
       "      <td>0.012360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NOUN.people</td>\n",
       "      <td>Entity</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>people</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NP</td>\n",
       "      <td>0.011673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NOUN.pea</td>\n",
       "      <td>Entity</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>peas</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Food</td>\n",
       "      <td>0.011634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NOUN.expert</td>\n",
       "      <td>Entity</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>experts</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NP</td>\n",
       "      <td>0.011456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NOUN.legume</td>\n",
       "      <td>Entity</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>legumes</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NP</td>\n",
       "      <td>0.010856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NOUN.soy NOUN.milk</td>\n",
       "      <td>Entity</td>\n",
       "      <td>NP</td>\n",
       "      <td>soy milk</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Food</td>\n",
       "      <td>0.010837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               key    kind    pos                text  chunk  \\\n",
       "1                    NOUN.dementia  Entity   NOUN            dementia      1   \n",
       "6                         PROPN.us  Entity  PROPN                  US      1   \n",
       "9                        NOUN.bean  Entity   NOUN               beans      1   \n",
       "22  VERB.process ADJ.red NOUN.meat  Entity     NP  processed red meat      1   \n",
       "8                         NOUN.nut  Entity   NOUN                nuts      1   \n",
       "2                       NOUN.study  Entity   NOUN               study      1   \n",
       "10                       NOUN.tofu  Entity   NOUN                tofu      1   \n",
       "0                        NOUN.risk  Entity   NOUN                risk      1   \n",
       "36               DET.the NOUN.risk  Entity     NP            the risk      1   \n",
       "28               ADJ.red NOUN.meat  Entity     NP            red meat      1   \n",
       "7                  NOUN.researcher  Entity   NOUN         researchers      1   \n",
       "27      ADJ.cognitive NOUN.decline  Entity     NP   cognitive decline      1   \n",
       "11                       NOUN.diet  Entity   NOUN                diet      1   \n",
       "44                DET.the PROPN.us  Entity     NP              the US      1   \n",
       "19                     NOUN.peanut  Entity   NOUN              peanut      1   \n",
       "3                      NOUN.people  Entity   NOUN              people      1   \n",
       "21                        NOUN.pea  Entity   NOUN                peas      1   \n",
       "12                     NOUN.expert  Entity   NOUN             experts      1   \n",
       "18                     NOUN.legume  Entity   NOUN             legumes      1   \n",
       "31              NOUN.soy NOUN.milk  Entity     NP            soy milk      1   \n",
       "\n",
       "    count         label      rank  \n",
       "1      12     Condition  0.034907  \n",
       "6       6       Country  0.024389  \n",
       "9       7          Food  0.023435  \n",
       "22      5          Food  0.023368  \n",
       "8       8          Food  0.022787  \n",
       "2       6      Research  0.021828  \n",
       "10      6          Food  0.021223  \n",
       "0       7            NP  0.021021  \n",
       "36      4            NP  0.020222  \n",
       "28      3          Food  0.018906  \n",
       "7       4  People Group  0.016184  \n",
       "27      2     Condition  0.014067  \n",
       "11      3            NP  0.013865  \n",
       "44      2            NP  0.012369  \n",
       "19      3          Food  0.012360  \n",
       "3       3            NP  0.011673  \n",
       "21      2          Food  0.011634  \n",
       "12      2            NP  0.011456  \n",
       "18      2            NP  0.010856  \n",
       "31      1          Food  0.010837  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df: pd.DataFrame = pd.DataFrame([\n",
    "    node_attr\n",
    "    for node, node_attr in graph.nodes(data = True)\n",
    "    if node_attr[\"kind\"] == \"Entity\"\n",
    "]).sort_values(by = [ \"rank\", \"count\" ], ascending = False)\n",
    "\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3b9c670-45cf-4c06-a299-efeeaff63924",
   "metadata": {},
   "source": [
    "## Visualize the results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35c7683b-7470-4860-839a-fb12e70acba2",
   "metadata": {},
   "source": [
    "Use `pyvis` to provide an interactive visualization of both layers of the graph, so far..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bdc8a259-18b3-47ac-8911-96129684c0d6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-12T20:28:26.480050Z",
     "iopub.status.busy": "2024-08-12T20:28:26.479864Z",
     "iopub.status.idle": "2024-08-12T20:28:26.508457Z",
     "shell.execute_reply": "2024-08-12T20:28:26.508139Z",
     "shell.execute_reply.started": "2024-08-12T20:28:26.480035Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chunk.html\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"750px\"\n",
       "            src=\"chunk.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x37e56bb10>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_net: pyvis.network.Network = pyvis.network.Network(\n",
    "    height = \"750px\",\n",
    "    width = \"100%\",\n",
    "    notebook = True,\n",
    "    cdn_resources = \"remote\",\n",
    ")\n",
    "\n",
    "for node_id, node_attr in graph.nodes(data = True):\n",
    "    if node_attr[\"kind\"] == \"Entity\":\n",
    "        color: str = \"hsl(65, 46%, 58%)\"\n",
    "        size: int = round(200 * node_attr[\"rank\"])\n",
    "    else:\n",
    "        color = \"hsla(72, 10%, 90%, 0.95)\"\n",
    "        size = round(30 * node_attr[\"rank\"])\n",
    "\n",
    "    pv_net.add_node(\n",
    "        node_id,\n",
    "        label = node_attr[\"text\"],\n",
    "        title = node_attr.get(\"label\"),\n",
    "        color = color,\n",
    "        size = size,\n",
    "    )\n",
    "\n",
    "for src_node, dst_node, edge_attr in graph.edges(data = True):\n",
    "    pv_net.add_edge(\n",
    "        src_node,\n",
    "        dst_node,\n",
    "        title = edge_attr.get(\"rel\"),\n",
    "    )\n",
    "\n",
    "pv_net.toggle_physics(True)\n",
    "pv_net.show(\"chunk.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28be1d4e-8d13-47db-87ca-2b1271e25459",
   "metadata": {},
   "source": [
    "Show a cluster analysis of the _lexical graph_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1333aed0-43b1-46a8-9c38-b1b7e1057798",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-12T20:28:26.509066Z",
     "iopub.status.busy": "2024-08-12T20:28:26.508950Z",
     "iopub.status.idle": "2024-08-12T20:28:26.519777Z",
     "shell.execute_reply": "2024-08-12T20:28:26.519162Z",
     "shell.execute_reply.started": "2024-08-12T20:28:26.509053Z"
    }
   },
   "outputs": [],
   "source": [
    "communities: typing.Generator = nx.community.louvain_communities(graph)\n",
    "\n",
    "comm_map: typing.Dict[ int, int ] = {\n",
    "    node_id: i\n",
    "    for i, comm in enumerate(communities)\n",
    "    for node_id in comm\n",
    "}\n",
    "                                                                                                                            \n",
    "xkcd_colors: typing.List[ str ] = list(matplotlib.colors.XKCD_COLORS.values())\n",
    "\n",
    "colors: typing.List[ str ] = [\n",
    "    xkcd_colors[comm_map[n]]\n",
    "    for n in list(graph.nodes())\n",
    "]\n",
    "                                                                                                                                         \n",
    "labels: typing.Dict[ int, str ] = {\n",
    "    node_id: node_attr[\"text\"]\n",
    "    for node_id, node_attr in graph.nodes(data = True)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cf14b7d7-f125-4a94-926e-8f33efaef35e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-12T20:28:26.520505Z",
     "iopub.status.busy": "2024-08-12T20:28:26.520373Z",
     "iopub.status.idle": "2024-08-12T20:28:27.050139Z",
     "shell.execute_reply": "2024-08-12T20:28:27.049779Z",
     "shell.execute_reply.started": "2024-08-12T20:28:26.520488Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SPRING_DISTANCE: float = 2.5\n",
    "                                                                                                                                     \n",
    "nx.draw_networkx(\n",
    "    graph,\n",
    "    pos = nx.spring_layout(\n",
    "        graph,\n",
    "        k = SPRING_DISTANCE / len(communities),\n",
    "    ),\n",
    "    labels = labels,\n",
    "    node_color = colors,\n",
    "    edge_color = \"#bbb\",\n",
    "    with_labels = True,\n",
    "    font_size = 8,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ec4e1eb-0f74-4750-94ee-59ec02639d0c",
   "metadata": {},
   "source": [
    "## Tear down"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed295918-d7b0-4989-8f28-4bb982ebc4f1",
   "metadata": {},
   "source": [
    "How much did the global data structures grow?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f1c8d63e-41c5-4e94-b918-5e435f9d4b2f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-12T20:28:27.051015Z",
     "iopub.status.busy": "2024-08-12T20:28:27.050801Z",
     "iopub.status.idle": "2024-08-12T20:28:27.100731Z",
     "shell.execute_reply": "2024-08-12T20:28:27.100394Z",
     "shell.execute_reply.started": "2024-08-12T20:28:27.050999Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ic| len(known_lemma): 102\n",
      "ic| len(span_decoder): 101\n",
      "ic| len(graph.nodes()): 102\n"
     ]
    }
   ],
   "source": [
    "ic(len(known_lemma))\n",
    "ic(len(span_decoder))\n",
    "ic(len(graph.nodes()));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a02b6640-1ae4-46bb-944d-6488f138faaf",
   "metadata": {},
   "source": [
    "Stop the profiler and report the performance measures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bf17e9f6-7551-4550-9923-4366b93df033",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-12T20:28:27.101449Z",
     "iopub.status.busy": "2024-08-12T20:28:27.101320Z",
     "iopub.status.idle": "2024-08-12T20:28:27.442080Z",
     "shell.execute_reply": "2024-08-12T20:28:27.441813Z",
     "shell.execute_reply.started": "2024-08-12T20:28:27.101436Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  _     ._   __/__   _ _  _  _ _/_   Recorded: 13:28:05  Samples:  8545\n",
      " /_//_/// /_\\ / //_// / //_'/ //     Duration: 21.299    CPU time: 45.569\n",
      "/   _/                      v4.6.2\n",
      "\n",
      "Program: /Users/paco/src/strwythura/venv/lib/python3.11/site-packages/ipykernel_launcher.py -f /Users/paco/Library/Jupyter/runtime/kernel-df47d7cc-9078-4e05-afa2-717d56d72df6.json\n",
      "\n",
      "21.298 MainThread  <thread>:8691665920\n",
      "├─ 20.875 _run_module_as_main  <frozen runpy>:173\n",
      "│     [32 frames hidden]  <frozen runpy>, ipykernel_launcher, t...\n",
      "│        20.424 ZMQInteractiveShell.run_ast_nodes  IPython/core/interactiveshell.py:3418\n",
      "│        ├─ 12.008 <module>  ../../../../var/folders/8f/wmjh62kx07vfnc2h3rsbclq80000gn/T/ipykernel_90412/2312294158.py:1\n",
      "│        │  ├─ 11.741 English.add_pipe  spacy/language.py:764\n",
      "│        │  │     [143 frames hidden]  spacy, confection, glirel, huggingfac...\n",
      "│        │  └─ 0.267 load  spacy/__init__.py:27\n",
      "│        │        [6 frames hidden]  spacy, en_core_web_md\n",
      "│        ├─ 3.241 <module>  ../../../../var/folders/8f/wmjh62kx07vfnc2h3rsbclq80000gn/T/ipykernel_90412/1388022593.py:1\n",
      "│        │  └─ 3.241 make_entity  ../../../../var/folders/8f/wmjh62kx07vfnc2h3rsbclq80000gn/T/ipykernel_90412/683571985.py:16\n",
      "│        │     └─ 3.238 IceCreamDebugger.__call__  icecream/icecream.py:204\n",
      "│        │           [27 frames hidden]  icecream, colorama, ipykernel, thread...\n",
      "│        ├─ 3.020 <module>  ../../../../var/folders/8f/wmjh62kx07vfnc2h3rsbclq80000gn/T/ipykernel_90412/1732784421.py:1\n",
      "│        │  └─ 3.020 IceCreamDebugger.__call__  icecream/icecream.py:204\n",
      "│        │        [27 frames hidden]  icecream, colorama, ipykernel, thread...\n",
      "│        ├─ 1.179 <module>  ../../../../var/folders/8f/wmjh62kx07vfnc2h3rsbclq80000gn/T/ipykernel_90412/838091528.py:1\n",
      "│        │  └─ 1.179 English.pipe  spacy/language.py:1534\n",
      "│        │        [43 frames hidden]  spacy, glirel, torch, flair, transfor...\n",
      "│        └─ 0.624 <module>  ../../../../var/folders/8f/wmjh62kx07vfnc2h3rsbclq80000gn/T/ipykernel_90412/2668620869.py:1\n",
      "│           └─ 0.620 IceCreamDebugger.__call__  icecream/icecream.py:204\n",
      "│                 [12 frames hidden]  icecream, inspect, colorama, ipykernel\n",
      "└─ 0.317 [self]  <thread>\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "profiler.stop()\n",
    "profiler.print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5504bb1-e572-4a39-a1c3-d116ffccb2bd",
   "metadata": {},
   "source": [
    "## Quality checks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7923fad-8954-4bb2-a30d-2e74eae4c285",
   "metadata": {},
   "source": [
    "Are there any prounoun lemmas that we need to add to the `STOP_WORDS` list? Until we have a good _coreference_ stage in this workflow, the pronouns are too generic and tend to distort the graph results. NB: compound references are \"contained\" and not a problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b8bfd481-1db0-46c6-a3e5-34a86d8d9b5e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-12T20:28:27.442670Z",
     "iopub.status.busy": "2024-08-12T20:28:27.442586Z",
     "iopub.status.idle": "2024-08-12T20:28:27.444811Z",
     "shell.execute_reply": "2024-08-12T20:28:27.444399Z",
     "shell.execute_reply.started": "2024-08-12T20:28:27.442662Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PRON.their NOUN.diet\n",
      "PRON.their NOUN.consumption\n"
     ]
    }
   ],
   "source": [
    "for x in known_lemma:\n",
    "    if \"PRON\" in x:\n",
    "        print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1c8545e-57c9-4bc8-b9ce-bbf6f8616080",
   "metadata": {},
   "source": [
    "Which nodes should we promote to the next level?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c570f798-e5a3-4976-8d07-5049af8c307b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-12T20:28:27.445347Z",
     "iopub.status.busy": "2024-08-12T20:28:27.445263Z",
     "iopub.status.idle": "2024-08-12T20:28:27.448316Z",
     "shell.execute_reply": "2024-08-12T20:28:27.448098Z",
     "shell.execute_reply.started": "2024-08-12T20:28:27.445341Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 NOUN.risk 0.021020770833106227 NP risk 1\n",
      "3 NOUN.dementia 0.03490707847497394 Condition dementia 1\n",
      "4 NOUN.study 0.021827653697358264 Research study 1\n",
      "5 NOUN.people 0.011673015094542011 NP people 1\n",
      "7 NOUN.cancer 0.010277691029743925 Condition cancer 1\n",
      "10 NOUN.type 0.00979012786857144 NP type 1\n",
      "12 PROPN.us 0.02438925457091363 Country US 1\n",
      "13 NOUN.researcher 0.016183608171629935 People Group researchers 1\n",
      "16 NOUN.nut 0.022787427192470483 Food nuts 1\n",
      "17 NOUN.bean 0.02343545291750915 Food beans 1\n",
      "18 NOUN.tofu 0.02122261429986201 Food tofu 1\n",
      "24 NOUN.diet 0.013865170272879646 NP diet 1\n",
      "28 NOUN.expert 0.011456076867389033 NP experts 1\n",
      "33 NOUN.datum 0.008400769914764324 NP data 1\n",
      "35 NOUN.bacon 0.008779846264157088 Food bacon 1\n",
      "36 NOUN.hotdog 0.009720588341527564 Food hotdogs 1\n",
      "37 NOUN.sausage 0.008719053640216945 Food sausages 1\n",
      "38 NOUN.salami 0.008719053640216945 Food salami 1\n",
      "41 NOUN.legume 0.010855576570459784 NP legumes 1\n",
      "42 NOUN.peanut 0.01236037896235104 Food peanut 1\n",
      "44 NOUN.walnut 0.010752851726073107 Food walnuts 1\n",
      "46 NOUN.pea 0.011634347600796423 Food peas 1\n",
      "56 VERB.process ADJ.red NOUN.meat 0.02336835224855792 Food processed red meat 1\n",
      "57 ADJ.processed ADJ.red NOUN.meat 0.00756648028205884 Food Processed red meat 1\n",
      "58 NOUN.heart NOUN.disease 0.008729627340870448 Condition heart disease 1\n",
      "59 NOUN.type NUM.2 NOUN.diabetes 0.00784107106095278 Condition type 2 diabetes 1\n",
      "60 PROPN.alzheimer PART.’s PROPN.association ADJ.international NOUN.conference 0.010278569079316248 Conference Alzheimer’s Association international conference 1\n",
      "61 ADJ.cognitive NOUN.decline 0.01406686526762474 Condition cognitive decline 1\n",
      "62 ADJ.red NOUN.meat 0.01890582525321585 Food red meat 1\n",
      "63 NOUN.peanut NOUN.butter 0.00995558708256962 Food peanut butter 1\n",
      "64 NOUN.string NOUN.bean 0.009911419954518563 Food string beans 1\n",
      "65 NOUN.soy NOUN.milk 0.010836997463863952 Food soy milk 1\n",
      "66 DET.a ADJ.significant NOUN.risk NOUN.factor 0.007412463513177861 NP a significant risk factor 1\n",
      "67 DET.a ADJ.large NOUN.study 0.00648378000584349 NP a large study 1\n",
      "68 ADJ.more ADP.than NUM.100,000 NOUN.people 0.006582997916010268 NP more than 100,000 people 1\n",
      "69 NUM.four NOUN.decade 0.006746336998075852 NP four decades 1\n",
      "70 DET.the NOUN.risk 0.020221911953355782 NP the risk 1\n",
      "71 NUM.2 NOUN.diabetes 0.00784107106095278 NP 2 diabetes 1\n",
      "72 PROPN.us NOUN.researcher 0.0052345904838473675 NP US researchers 1\n",
      "73 DET.a ADJ.potential NOUN.link 0.005484650510464529 NP a potential link 1\n",
      "74 DET.the NOUN.study 0.009999512987266295 NP The study 1\n",
      "75 ADJ.healthy NOUN.food 0.007213441414522436 NP healthier foods 1\n",
      "76 DET.the NOUN.finding 0.008083769036277624 NP The findings 1\n",
      "77 DET.the PROPN.alzheimer PART.’s PROPN.association ADJ.international NOUN.conference 0.01027856907931625 NP the Alzheimer’s Association international conference 1\n",
      "78 DET.the PROPN.us 0.012369205677130011 NP the US 1\n",
      "79 DET.the NOUN.number 0.008839745028539072 NP The number 1\n",
      "80 DET.a NOUN.focus 0.008803435130955758 NP a focus 1\n",
      "81 DET.the ADJ.late NOUN.research 0.009139886436992298 NP the latest research 1\n",
      "82 DET.the NOUN.health 0.009008661151407008 NP the health 1\n",
      "83 NUM.130,000 NOUN.nurse 0.009055998359107548 NP 130,000 nurses 1\n",
      "84 ADJ.other NOUN.health NOUN.worker 0.01025342425960202 NP other health workers 1\n",
      "85 NUM.43 NOUN.year 0.007358453385158796 NP 43 years 1\n",
      "86 PRON.their NOUN.diet 0.006934427729754546 NP their diet 1\n",
      "87 DET.every NUM.2 PART.to NUM.5 NOUN.year 0.007358453385158796 NP every 2 to 5 years 1\n",
      "88 DET.the NOUN.participant 0.008008493003654743 NP The participants 1\n",
      "89 ADJ.other NOUN.sandwich NOUN.meat 0.008719053640216945 NP other sandwich meat 1\n",
      "90 PRON.their NOUN.consumption 0.010013622129533939 NP their consumption 1\n",
      "91 ADJ.other NOUN.nut 0.009114062006865173 NP other nuts 1\n",
      "92 ADJ.more ADP.than NUM.11,000 NOUN.case 0.005244305195464342 NP More than 11,000 cases 1\n",
      "93 DET.the VERB.follow PUNCT.- ADP.up NOUN.period 0.005244305195464342 NP the follow-up period 1\n",
      "94 NUM.two NOUN.serving 0.007467947593906382 NP two servings 1\n",
      "95 NUM.14 NOUN.% 0.00754593313186887 NP 14% 1\n",
      "96 ADV.about NUM.three NOUN.serving 0.007467947593906382 NP about three servings 1\n",
      "97 DET.the NOUN.researcher 0.007539847015782026 NP the researchers 1\n",
      "98 NUM.one ADV.daily NOUN.serving 0.009965234125990544 NP one daily serving 1\n",
      "99 DET.a ADJ.daily NOUN.serving 0.009965234125990544 NP a daily serving 1\n",
      "100 NUM.23 NOUN.% 0.010045799583812805 NP 23% 1\n",
      "101 DET.the PROPN.alzheimer PART.’s PROPN.association 0.00910447637450513  the Alzheimer’s Association 1\n"
     ]
    }
   ],
   "source": [
    "kept_nodes: typing.Set[ int ] = set()\n",
    "\n",
    "for node_id, node_attr in graph.nodes(data = True):\n",
    "    if node_attr[\"kind\"] == \"Entity\":\n",
    "        print(node_id, node_attr[\"key\"], node_attr[\"rank\"], node_attr[\"label\"], node_attr[\"text\"], node_attr[\"chunk\"])\n",
    "        kept_nodes.add(node_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9ca39ea-565f-4325-98fa-712208ab255d",
   "metadata": {},
   "source": [
    "Which edges should we promote to the next level?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1db93959-90b7-4967-b0c9-f7bca8b3fc23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-12T20:28:27.448714Z",
     "iopub.status.busy": "2024-08-12T20:28:27.448633Z",
     "iopub.status.idle": "2024-08-12T20:28:27.452496Z",
     "shell.execute_reply": "2024-08-12T20:28:27.452289Z",
     "shell.execute_reply.started": "2024-08-12T20:28:27.448708Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "78 83 CO_OCCURS_WITH 1.0\n",
      "78 84 CO_OCCURS_WITH 1.0\n",
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      "88 89 CO_OCCURS_WITH 1.0\n",
      "90 91 CO_OCCURS_WITH 1.0\n",
      "92 93 CO_OCCURS_WITH 1.0\n",
      "94 96 CO_OCCURS_WITH 1.0\n",
      "94 97 CO_OCCURS_WITH 1.0\n",
      "94 95 CO_OCCURS_WITH 1.0\n",
      "95 96 CO_OCCURS_WITH 1.0\n",
      "95 97 CO_OCCURS_WITH 1.0\n",
      "96 97 CO_OCCURS_WITH 1.0\n",
      "98 99 CO_OCCURS_WITH 1.0\n",
      "98 100 CO_OCCURS_WITH 1.0\n",
      "99 100 CO_OCCURS_WITH 1.0\n"
     ]
    }
   ],
   "source": [
    "skip_rel: typing.Set[ str ] = set([ \"FOLLOWS_LEXICALLY\", \"COMPOUND_ELEMENT_OF\" ])\n",
    "\n",
    "for src_id, dst_id, edge_attr in graph.edges(data = True):\n",
    "    if src_id in kept_nodes and dst_id in kept_nodes:\n",
    "        rel: str = edge_attr[\"rel\"]\n",
    "\n",
    "        if rel not in skip_rel:\n",
    "            print(src_id, dst_id, rel, edge_attr[\"prob\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2aede50a-b14d-4c9b-9bdd-1efd2262f66f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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