# A Tidy Data Model for Natural Language Processing using cleanNLP

arXiv: https://arxiv.org/abs/1703.09570 · HTML: https://mlsanity.com/paper/1703.09570/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2017.

## Built on by (most cited first, 2 of 2)

- [CAIT: A Syntactic Parsing Toolkit for Child-Adult InTeractions](https://mlsanity.com/paper/2605.19718/) (2026)
- [The R package sentometrics to compute, aggregate and predict with textual sentiment](https://mlsanity.com/paper/2110.10817/) (2021)

## Abstract (arXiv)

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.