Improving Aspect-Based Sentiment with End-to-End Semantic Role Labeling Model
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43970051" target="_blank" >RIV/49777513:23520/23:43970051 - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2023.ranlp-1.96/" target="_blank" >https://aclanthology.org/2023.ranlp-1.96/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.26615/978-954-452-092-2_096" target="_blank" >10.26615/978-954-452-092-2_096</a>
Alternative languages
Result language
angličtina
Original language name
Improving Aspect-Based Sentiment with End-to-End Semantic Role Labeling Model
Original language description
This paper presents a series of approaches aimed at enhancing the performance of Aspect-Based Sentiment Analysis (ABSA) by utilizing extracted semantic information from a Semantic Role Labeling (SRL) model. We propose a novel end-to-end Semantic Role Labeling model that effectively captures most of the structured semantic information within the Transformer hidden state. We believe that this end-to-end model is well-suited for our newly proposed models that incorporate semantic information. We evaluate the proposed models in two languages, English and Czech, employing ELECTRA-small models. Our combined models improve ABSA performance in both languages. Moreover, we achieved new state-of-the-art results on the Czech ABSA.
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
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Deep Learning for Natural Language Processing Methods and Applications
ISBN
978-954-452-092-2
ISSN
1313-8502
e-ISSN
2603-2813
Number of pages
10
Pages from-to
888-897
Publisher name
INCOMA Ltd.
Place of publication
Shoumen
Event location
Varna
Event date
Sep 4, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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