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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

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

2024

Kód důvěrnosti údajů

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

Název periodika

Communication Methods and Measures

ISSN

1931-2458

e-ISSN

—

Svazek periodika

18

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

19

Strana od-do

371-389

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85188524285