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.
Features
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!
Links
Note: ReadTheDocs builds have been failing for months, so those docs are currently out-of-date.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:
Maintainer
Howdy, y’all. 👋
Burton DeWilde (<burton@chartbeat.com>)
1 maintainer
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