Incorporating Syntax and Lexical Knowledge to Multilingual Sentiment Classification on Large Language Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3ARAABDZCE" target="_blank" >RIV/00216208:11320/25:RAABDZCE - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205305997&partnerID=40&md5=5c611bb12243efaadecc259376a6217f" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205305997&partnerID=40&md5=5c611bb12243efaadecc259376a6217f</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Incorporating Syntax and Lexical Knowledge to Multilingual Sentiment Classification on Large Language Models
Original language description
This paper exploits a sentiment extractor supported by syntactic and lexical resources to enhance multilingual sentiment classification solved through the generative approach, without retraining LLMs. By adding external information of words and phrases that have positive/negative polarities, the multilingual sentiment classification error was reduced by up to 33 points, and the combination of two approaches performed best especially in high-performing pairs of LLMs and languages. © 2024 Association for Computational Linguistics.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
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
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proc. Annu. Meet. Assoc. Comput Linguist.
ISBN
979-889176099-8
ISSN
0736-587X
e-ISSN
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Number of pages
8
Pages from-to
4810-4817
Publisher name
Association for Computational Linguistics (ACL)
Place of publication
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Event location
Hybrid, Bangkok
Event date
Jan 1, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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