Predictive keywords: Using machine learning to explain document characteristics

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AJ6AK6WYQ" target="_blank" >RIV/00216208:11320/23:J6AK6WYQ - isvavai.cz</a>

Výsledek na webu

<a href="https://www.frontiersin.org/articles/10.3389/frai.2022.975729/full" target="_blank" >https://www.frontiersin.org/articles/10.3389/frai.2022.975729/full</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3389/frai.2022.975729" target="_blank" >10.3389/frai.2022.975729</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Predictive keywords: Using machine learning to explain document characteristics

Popis výsledku v původním jazyce

"When exploring the characteristics of a discourse domain associated with texts, keyword analysis is widely used in corpus linguistics. However, one of the challenges facing this method is the evaluation of the quality of the keywords. Here, we propose casting keyword analysis as a prediction problem with the goal of discriminating the texts associated with the target corpus from the reference corpus. We demonstrate that, when using linear support vector machines, this approach can be used not only to quantify the discrimination between the two corpora, but also extract keywords. To evaluate the keywords, we develop a systematic and rigorous approach anchored to the concepts of usefulness and relevance used in machine learning. The extracted keywords are compared with the recently proposed text dispersion keyness measure. We demonstrate that that our approach extracts keywords that are highly useful and linguistically relevant, capturing the characteristics of their discourse domain."

Název v anglickém jazyce

Predictive keywords: Using machine learning to explain document characteristics

Popis výsledku anglicky

"When exploring the characteristics of a discourse domain associated with texts, keyword analysis is widely used in corpus linguistics. However, one of the challenges facing this method is the evaluation of the quality of the keywords. Here, we propose casting keyword analysis as a prediction problem with the goal of discriminating the texts associated with the target corpus from the reference corpus. We demonstrate that, when using linear support vector machines, this approach can be used not only to quantify the discrimination between the two corpora, but also extract keywords. To evaluate the keywords, we develop a systematic and rigorous approach anchored to the concepts of usefulness and relevance used in machine learning. The extracted keywords are compared with the recently proposed text dispersion keyness measure. We demonstrate that that our approach extracts keywords that are highly useful and linguistically relevant, capturing the characteristics of their discourse domain."

Druh

J_ost - Ostatní články v recenzovaných periodicích

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

2023

Kód důvěrnosti údajů

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

Název periodika

"Frontiers in Artificial Intelligence"

ISSN

2624-8212

e-ISSN

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Svazek periodika

5

Číslo periodika v rámci svazku

2023-3-9

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

23

Strana od-do

1-23

Kód UT WoS článku

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EID výsledku v databázi Scopus

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