refactoring to work with the annotated plain text #18
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… various industries.
…es_from_files` to `load_trees`.
webstruct/feature_extraction.py
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| @@ -3,7 +3,7 @@ | |||
| :mod:`webstruct.feature_extraction` contains classes that help | |||
| with: | |||
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| - converting HTML pages into lists of feature dicts and | |||
| - converting annnotated data into lists of feature dicts and | |||
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the data is not necessarily annotated: HtmlLoader is used to load raw data
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My main concern in Token class and TextTokenizer thing. Creating Token instances looks like a total overkill - why would anyone need to wrap text token in Token instance and to keep reference to all other tokens in the text there? Also, there is already a text_tokenizers module, so this adds to confusion. |
HtmlFeatureExtractor to FeatureExtractor
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sometime the training data maybe plain text, instead of using python-crfsuite or any other CRF package, i still prefer to use webstruct because it has sklearn
pipelineand some evaluation tools out of box.the input text annotated text is similar to GATE: e.g.
this is a <NER>test</NER>. the entities are surrounded by <> tags. the rest of the change just moving the generic code to a more proper place.