Enhanced Packed Marker with Entity Information for Aspect Sentiment Triplet Extraction

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Enhanced Packed Marker with Entity Information for Aspect Sentiment Triplet Extraction

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Enhanced Packed Marker with Entity Information for Aspect Sentiment Triplet Extraction

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

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 statě ve sborníku

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

ISBN

979-840070431-4

ISSN

—

e-ISSN

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Počet stran výsledku

11

Strana od-do

619-629

Název nakladatele

Association for Computing Machinery, Inc

Místo vydání

—

Místo konání akce

Washington D.C.

Datum konání akce

1. 1. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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