Transferring Sentiment Cross-Lingually within and across Same-Family Languages
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AHH22QJHV" target="_blank" >RIV/00216208:11320/25:HH22QJHV - isvavai.cz</a>
Výsledek na webu
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198437325&doi=10.3390%2fapp14135652&partnerID=40&md5=2719fc18f07454cfce40f5288d37e75d" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198437325&doi=10.3390%2fapp14135652&partnerID=40&md5=2719fc18f07454cfce40f5288d37e75d</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/app14135652" target="_blank" >10.3390/app14135652</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Transferring Sentiment Cross-Lingually within and across Same-Family Languages
Popis výsledku v původním jazyce
Natural language processing for languages with limited resources is hampered by a lack of data. Using English as a hub language for such languages, cross-lingual sentiment analysis has been developed. The sheer quantity of English language resources raises questions about its status as the primary resource. This research aims to examine the impact on sentiment analysis of adding data from same-family versus distant-family languages. We analyze the performance using low-resource and high-resource data from the same language family (Slavic), investigate the effect of using a distant-family language (English) and report the results for both settings. Quantitative experiments using multi-task learning demonstrate that adding a large quantity of data from related and distant-family languages is advantageous for cross-lingual sentiment transfer. © 2024 by the authors.
Název v anglickém jazyce
Transferring Sentiment Cross-Lingually within and across Same-Family Languages
Popis výsledku anglicky
Natural language processing for languages with limited resources is hampered by a lack of data. Using English as a hub language for such languages, cross-lingual sentiment analysis has been developed. The sheer quantity of English language resources raises questions about its status as the primary resource. This research aims to examine the impact on sentiment analysis of adding data from same-family versus distant-family languages. We analyze the performance using low-resource and high-resource data from the same language family (Slavic), investigate the effect of using a distant-family language (English) and report the results for both settings. Quantitative experiments using multi-task learning demonstrate that adding a large quantity of data from related and distant-family languages is advantageous for cross-lingual sentiment transfer. © 2024 by the authors.
Klasifikace
Druh
J<sub>SC</sub> - 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)
Návaznosti výsledku
Projekt
—
Návaznosti
—
Ostatní
Rok uplatnění
2024
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Applied Sciences (Switzerland)
ISSN
2076-3417
e-ISSN
—
Svazek periodika
14
Číslo periodika v rámci svazku
13
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
21
Strana od-do
1-21
Kód UT WoS článku
—
EID výsledku v databázi Scopus
2-s2.0-85198437325