Papers › A Tidy Data Model for Natural Language Processing using cleanNLP

A Tidy Data Model for Natural Language Processing using cleanNLP

27 Mar 2017arXiv:1703.09570archive 2025-07-28

Taylor Arnold

The package cleanNLP provides a set of fast tools for converting a textual corpus into a set of normalized tables. The underlying natural language processing pipeline utilizes Stanford's CoreNLP library, exposing a number of annotation tasks for text written in English, French, German, and Spanish. Annotators include tokenization, part of speech tagging, named entity recognition, entity linking, sentiment analysis, dependency parsing, coreference resolution, and information extraction.

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Coreference Resolution Dependency Parsing Entity Linking Named Entity Recognition Named Entity Recognition (NER)Part-Of-Speech Tagging Sentiment Analysis coreference-resolution named-entity-recognition

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