Comparison of text preprocessing methods

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/00216208:11320/23:PHNVYRMF

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of text preprocessing methods

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Comparison of text preprocessing methods

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Natural Language Engineering

ISSN

1351-3249

e-ISSN

1469-8110

Svazek periodika

28

Číslo periodika v rámci svazku

2022-6-13

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

45

Strana od-do

1-45

Kód UT WoS článku

000809676200001

EID výsledku v databázi Scopus

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