Automatically Finding Actors in Texts: A Performance Review of Multilingual Named Entity Recognition Tools

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AJ2XNL4A2" target="_blank" >RIV/00216208:11320/25:J2XNL4A2 - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188524285&doi=10.1080%2f19312458.2024.2324789&partnerID=40&md5=3c784997e2fadd2d84b0d7923affcad0" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188524285&doi=10.1080%2f19312458.2024.2324789&partnerID=40&md5=3c784997e2fadd2d84b0d7923affcad0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/19312458.2024.2324789" target="_blank" >10.1080/19312458.2024.2324789</a>

Result language

angličtina

Original language name

Automatically Finding Actors in Texts: A Performance Review of Multilingual Named Entity Recognition Tools

Original language description

Named Entity Recognition (NER) is a crucial task in natural language processing and has a wide range of applications in communication science. However, there is a lack of systematic evaluations of available NER tools in the field. In this study, we evaluate the performance of various multilingual NER tools, including rule-based and transformer-based models. We conducted experiments on corpora containing texts in multiple languages and evaluated the F1-score, speed, and features of each tool. Our results show that transformer-based language models outperform rule-based models and other NER tools in most languages. However, we found that the performance of the transformer-based models varies depending on the language and the corpus. Our study provides insights into the strengths and weaknesses of NER tools and their suitability for specific languages, which can inform the selection of appropriate tools for future studies and applications in communication science. © 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS 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)

Project

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Continuities

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Publication year

2024

Confidentiality

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

Name of the periodical

Communication Methods and Measures

ISSN

1931-2458

e-ISSN

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Volume of the periodical

18

Issue of the periodical within the volume

4

Country of publishing house

US - UNITED STATES

Number of pages

19

Pages from-to

371-389

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85188524285