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{ | ||
"@context": "http://clowder.ncsa.illinois.edu/contexts/extractors.jsonld", | ||
"name": "smm.name.entity.recognition", | ||
"version": "0.1.1", | ||
"version": "0.1.2", | ||
"description": "Named-entity recognition (NER) (also known as entity identification, entity chunking and entity extraction) is a subtask of information extraction that seeks to locate and classify named entity mentions in unstructured text into pre-defined categories such as the person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.", | ||
"author": "Wang, Chen <[email protected]>", | ||
"contributors": [], | ||
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{ | ||
"@context": "http://clowder.ncsa.illinois.edu/contexts/extractors.jsonld", | ||
"name": "smm.preprocessing.analysis", | ||
"version": "0.1.1", | ||
"version": "0.1.2", | ||
"description": "Tokenization is the process of dividing written text into meaningful units, such as words, sentences , or topics. Lemmatization and Stemming reduces word forms to common base words. Part-of-speech Tagging is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context.", | ||
"author": "Wang, Chen <[email protected]>", | ||
"contributors": [], | ||
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{ | ||
"@context": "http://clowder.ncsa.illinois.edu/contexts/extractors.jsonld", | ||
"name": "smm.sentiment.analysis", | ||
"version": "0.1.1", | ||
"version": "0.1.2", | ||
"description": "Sentiment analysis (sometimes known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.", | ||
"author": "Wang, Chen <[email protected]>", | ||
"contributors": [], | ||
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{ | ||
"@context": "http://clowder.ncsa.illinois.edu/contexts/extractors.jsonld", | ||
"name": "smm.topic.modeling", | ||
"version": "0.1.1", | ||
"version": "0.1.2", | ||
"description": "One of the primary applications of natural language processing is to automatically extract what topics people are discussing from large volumes of text. Topic modeling is a type of statistical modeling for discovering the abstract topics that occur in a collection of documents. Latent Dirichlet Allocation (LDA) is an example of topic model and is used to classify text in a document to a particular topic. It builds a topic per document model and words per topic model, modeled as Dirichlet distributions.", | ||
"author": "Wang, Chen <[email protected]>", | ||
"contributors": [], | ||
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