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 on the tasks that come before and follow after.
- Provide a convenient entry point and interface to one or many documents, with the core processing delegated to spaCy
- Stream text, json, csv, spaCy binary, and other data to and from disk
- Download and explore a variety of included datasets with both text content and metadata, from Congressional speeches to historical literature to Reddit comments
- Clean and normalize raw text, before analyzing it
- Access and filter basic linguistic elements, such as words, ngrams, and noun chunks; extract named entities, acronyms and their definitions, and key terms
- Flexibly tokenize and vectorize documents and corpora, then train, interpret, and visualize topic models using LSA, LDA, or NMF methods
- Compare strings, sets, and documents by a variety of similarity metrics
- Calculate common text statistics, including Flesch-Kincaid Grade Level, SMOG Index, and multilingual Flesch Reading Ease
... and more!
Note: ReadTheDocs builds have been failing for months, so those docs are currently out-of-date. Very sorry. As a (temporary?) workaround, docs for the latest version (v0.6.0) have been published via GitHub Pages:
https://chartbeat-labs.github.io/textacy
Howdy, y'all. 👋
- Burton DeWilde (<burton@chartbeat.com>)