NLP, before and after spaCy
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.
- Convenient entry points to working with one or many documents processed by spaCy, with functionality added via custom extensions and automatic language identification for applying the right spaCy pipeline
- Variety of downloadable datasets with both text content and metadata, from Congressional speeches to historical literature to Reddit comments
- Easy file I/O for streaming data to and from disk
- Cleaning, normalization, and exploration of raw text — before processing
- Flexible extraction of words, ngrams, noun chunks, entities, acronyms, key terms, and other elements of interest
- Tokenization and vectorization of documents, with functionality for training, interpreting, and visualizing topic models
- String, set, and document similarity comparison by a variety of metrics
- Calculations for common text statistics, including Flesch-Kincaid Grade Level and multilingual Flesch Reading Ease
... and more!
- Download: https://pypi.org/project/textacy
- Documentation: https://chartbeat-labs.github.io/textacy
- Source code: https://github.com/chartbeat-labs/textacy
- Bug Tracker: https://github.com/chartbeat-labs/textacy/issues
Howdy, y'all. 👋
- Burton DeWilde (email@example.com)
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