## textacy: NLP, before and after spaCy
`textacy` is a Python library for performing a variety of natural language processing (NLP) tasks, built on the high-performance spaCy library. With the fundamentals --- tokenization, part-of-speech tagging, dependency parsing, etc. --- delegated to another library, `textacy` focuses primarily on the tasks that come before and follow after.
[](https://travis-ci.org/chartbeat-labs/textacy)
[](https://github.com/chartbeat-labs/textacy/releases)
[](https://pypi.python.org/pypi/textacy)
[](https://anaconda.org/conda-forge/textacy)
### features
- Access and extend spaCy's core functionality for working with one or many documents through convenient methods and custom extensions
- Load prepared datasets with both text content and metadata, from Congressional speeches to historical literature to Reddit comments
- Clean, normalize, and explore raw text before processing it with spaCy
- Extract structured information from processed documents, including n-grams, entities, acronyms, keyterms, and SVO triples
- Compare strings and sequences using a variety of similarity metrics
- Tokenize and vectorize documents then train, interpret, and visualize topic models
- Compute text readability and lexical diversity statistics, including Flesch-Kincaid grade level, multilingual Flesch Reading Ease, and Type-Token Ratio
... *and much more!*
### links
- Download: https://pypi.org/project/textacy
- Documentation: https://textacy.readthedocs.io
- Source code: https://github.com/chartbeat-labs/textacy
- Bug Tracker: https://github.com/chartbeat-labs/textacy/issues
### maintainer
Howdy, y'all. 👋
- Burton DeWilde (<burtdewilde@gmail.com>)
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"description": "## textacy: NLP, before and after spaCy\n\n`textacy` is a Python library for performing a variety of natural language processing (NLP) tasks, built on the high-performance spaCy library. With the fundamentals --- tokenization, part-of-speech tagging, dependency parsing, etc. --- delegated to another library, `textacy` focuses primarily on the tasks that come before and follow after.\n\n[](https://travis-ci.org/chartbeat-labs/textacy)\n[](https://github.com/chartbeat-labs/textacy/releases)\n[](https://pypi.python.org/pypi/textacy)\n[](https://anaconda.org/conda-forge/textacy)\n\n### features\n\n- Access and extend spaCy's core functionality for working with one or many documents through convenient methods and custom extensions\n- Load prepared datasets with both text content and metadata, from Congressional speeches to historical literature to Reddit comments\n- Clean, normalize, and explore raw text before processing it with spaCy\n- Extract structured information from processed documents, including n-grams, entities, acronyms, keyterms, and SVO triples\n- Compare strings and sequences using a variety of similarity metrics\n- Tokenize and vectorize documents then train, interpret, and visualize topic models\n- Compute text readability and lexical diversity statistics, including Flesch-Kincaid grade level, multilingual Flesch Reading Ease, and Type-Token Ratio\n\n... *and much more!*\n\n### links\n\n- Download: https://pypi.org/project/textacy\n- Documentation: https://textacy.readthedocs.io\n- Source code: https://github.com/chartbeat-labs/textacy\n- Bug Tracker: https://github.com/chartbeat-labs/textacy/issues\n\n### maintainer\n\nHowdy, y'all. \ud83d\udc4b\n\n- Burton DeWilde (<burtdewilde@gmail.com>)\n",
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