Predictive keywords: Using machine learning to explain document characteristics
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Predictive keywords: Using machine learning to explain document characteristics
Original language description
"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."
Czech name
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Czech description
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Classification
Type
J<sub>ost</sub> - Miscellaneous article in a specialist periodical
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)
Result continuities
Project
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Continuities
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Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
"Frontiers in Artificial Intelligence"
ISSN
2624-8212
e-ISSN
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Volume of the periodical
5
Issue of the periodical within the volume
2023-3-9
Country of publishing house
US - UNITED STATES
Number of pages
23
Pages from-to
1-23
UT code for WoS article
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EID of the result in the Scopus database
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