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.

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)