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.
textacy focuses on tasks facilitated by the availability of tokenized, POS-tagged, and parsed text.
Features include:
- Convenient entry points to working with one or many documents processed by spaCy, with functionality added via custom extensions.
- Variety of downloadable datasets with both text content and metadata, from Congressional speeches to historical literature.
- 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.
Website: textacy.readthedocs.io
Support: GitHub Code Repository
Developer: Chartbeat, Inc.
License: Apache License, Version 2.0
textacy is written in Python. Learn Python with our recommended free books and free tutorials.
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