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Comparison of text preprocessing methods

Published online by Cambridge University Press:  13 June 2022

Christine P. Chai Open the ORCID record for Christine P. Chai [Opens in a new window]
Christine P. Chai*
Affiliation:
Microsoft Corporation, One Microsoft Way, Redmond, WA 98052, USA
*
E-mail: [email protected]
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Abstract

Text preprocessing is not only an essential step to prepare the corpus for modeling but also a key area that directly affects the natural language processing (NLP) application results. For instance, precise tokenization increases the accuracy of part-of-speech (POS) tagging, and retaining multiword expressions improves reasoning and machine translation. The text corpus needs to be appropriately preprocessed before it is ready to serve as the input to computer models. The preprocessing requirements depend on both the nature of the corpus and the NLP application itself, that is, what researchers would like to achieve from analyzing the data. Conventional text preprocessing practices generally suffice, but there exist situations where the text preprocessing needs to be customized for better analysis results. Hence, we discuss the pros and cons of several common text preprocessing methods: removing formatting, tokenization, text normalization, handling punctuation, removing stopwords, stemming and lemmatization, n-gramming, and identifying multiword expressions. Then, we provide examples of text datasets which require special preprocessing and how previous researchers handled the challenge. We expect this article to be a starting guideline on how to select and fine-tune text preprocessing methods.

Keywords

Data preprocessingParsingText data mining
Type
Survey Paper
Information
Natural Language Engineering , Volume 29 , Issue 3 , May 2023 , pp. 509 - 553
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© The Author(s), 2022. Published by Cambridge University Press

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