Comparison of text preprocessing methods

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AHXXS9Z4V" target="_blank" >RIV/00216208:11320/22:HXXS9Z4V - isvavai.cz</a>
Alternative codes found
RIV/00216208:11320/23:PHNVYRMF
Result on the web
<a href="http://www.cambridge.org/core/journals/natural-language-engineering/article/comparison-of-text-preprocessing-methods/43A20821D65F1C0C4366B126FC794AE3" target="_blank" >http://www.cambridge.org/core/journals/natural-language-engineering/article/comparison-of-text-preprocessing-methods/43A20821D65F1C0C4366B126FC794AE3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1017/S1351324922000213" target="_blank" >10.1017/S1351324922000213</a>

Result language
angličtina
Original language name
Comparison of text preprocessing methods
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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