Enhanced Packed Marker with Entity Information for Aspect Sentiment Triplet Extraction

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A38KR6MH4" target="_blank" >RIV/00216208:11320/25:38KR6MH4 - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200546378&doi=10.1145%2f3626772.3657734&partnerID=40&md5=3bee1c871d1a0ec014a3f9e9437fede4" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200546378&doi=10.1145%2f3626772.3657734&partnerID=40&md5=3bee1c871d1a0ec014a3f9e9437fede4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3626772.3657734" target="_blank" >10.1145/3626772.3657734</a>

Result language

angličtina

Original language name

Enhanced Packed Marker with Entity Information for Aspect Sentiment Triplet Extraction

Original language description

Aspect sentiment triplet extraction (ASTE) is an emerging sentiment analysis task that aims to extract sentiment triplets from review sentences. Each sentiment triplet consists of an aspect, corresponding opinion, and sentiment. Although extensive research has been conducted on the ASTE task, existing methods use the span representations to predict the relationship between spans, failing to consider the interrelation between span pairs. On the other hand, early fusion of entity information is critical for sentiment classification. In this paper, we propose an Enhanced Packed Marker with Entity Information (EPMEI) framework for ASTE task to address the above limitations of the existing works. Specifically, EPMEI consists of entity recognition and sentiment classification models. The entity information is obtained from the entity recognition model first. After that, we insert solid markers with entity information at the input layer of the sentiment classification model to highlight the subject span and improve subject span representation. Furthermore, we introduce a subject-oriented packing strategy, which packs each subject span and all its levitated markers of object spans to model the interrelation between the same-subject span pairs. Extensive experimental results on four ASTE benchmark datasets demonstrate that EPMEI achieves the state-of-the-art baseline. Our code can be found in https://github.com/MKMaS-GUET/EPMEI. © 2024 ACM.

Czech name

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Czech description

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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)

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

Article name in the collection

SIGIR - Proc. Int. ACM SIGIR Conf. Res. Dev. Inf. Retr.

ISBN

979-840070431-4

ISSN

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e-ISSN

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Number of pages

11

Pages from-to

619-629

Publisher name

Association for Computing Machinery, Inc

Place of publication

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Event location

Washington D.C.

Event date

Jan 1, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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