Comparison of text preprocessing methods

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3APHNVYRMF" target="_blank" >RIV/00216208:11320/23:PHNVYRMF - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/22:HXXS9Z4V

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161324782&doi=10.1017%2fS1351324922000213&partnerID=40&md5=3a4598d2c03a8beaa6fa1607525887e2" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161324782&doi=10.1017%2fS1351324922000213&partnerID=40&md5=3a4598d2c03a8beaa6fa1607525887e2</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. © The Author(s), 2022. Published by Cambridge University Press."

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. © The Author(s), 2022. Published by Cambridge University Press."

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

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

2023

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

—

Svazek periodika

29

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

45

Strana od-do

509-553

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85161324782