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NLP, before and after spaCy

Project description

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.

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  • 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!



Howdy, y'all. 👋

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