textacy: higher-level NLP built on spaCy
textacy is a Python library for performing higher-level natural language processing (NLP) tasks, built on the high-performance spaCy library. With the basics — tokenization, part-of-speech tagging, dependency parsing, etc. — offloaded to another library, textacy focuses on tasks facilitated by the ready availability of tokenized, POS-tagged, and parsed text.
Stream text, json, csv, and spaCy binary data to and from disk
Clean and normalize raw text, before analyzing it
Explore included corpora of Congressional speeches and Supreme Court decisions, or stream documents from standard Wikipedia pages and Reddit comments datasets
Access and filter basic linguistic elements, such as words and ngrams, noun chunks and sentences
Extract named entities, acronyms and their definitions, direct quotations, key terms, and more from documents
Compare strings, sets, and documents by a variety of similarity metrics
Transform documents and corpora into vectorized and semantic network representations
Train, interpret, visualize, and save sklearn-style topic models using LSA, LDA, or NMF methods
Identify a text’s language, display key words in context (KWIC), true-case words, and navigate a parse tree
… and more!