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short-paper

Training Entire-Space Models for Target-oriented Opinion Words Extraction

Authors:

Sheng-Hua ZhongAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1875 - 1879

https://doi.org/10.1145/3477495.3531768

Published: 07 July 2022 Publication History

Abstract

Target-oriented opinion words extraction (TOWE) is a subtask of aspect-based sentiment analysis (ABSA). Given a sentence and an aspect term occurring in the sentence, TOWE extracts the corresponding opinion words for the aspect term. TOWE has two types of instance. In the first type, aspect terms are associated with at least one opinion word, while in the second type, aspect terms do not have corresponding opinion words. However, previous researches trained and evaluated their models with only the first type of instance, resulting in a sample selection bias problem. Specifically, TOWE models were trained with only the first type of instance, while these models would be utilized to make inference on the entire space with both the first type of instance and the second type of instance. Thus, the generalization performance will be hurt. Moreover, the performance of these models on the first type of instance cannot reflect their performance on entire space. To validate the sample selection bias problem, four popular TOWE datasets containing only aspect terms associated with at least one opinion word are extended and additionally include aspect terms without corresponding opinion words. Experimental results on these datasets show that training TOWE models on entire space will significantly improve model performance and evaluating TOWE models only on the first type of instance will overestimate model performance.

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Digital Library

Cited By

Huang JLi XDu YFan YHuang DChen X(2024)An aspect-opinion joint extraction model for target-oriented opinion words extraction on global spaceApplied Intelligence10.1007/s10489-024-05865-555:1Online publication date: 25-Nov-2024
https://doi.org/10.1007/s10489-024-05865-5
Wang PTao LTang MZhao MWang LXu YTian JMeng K(2023)A novel adaptive marker segmentation graph convolutional network for aspect-level sentiment analysisKnowledge-Based Systems10.1016/j.knosys.2023.110559270:COnline publication date: 21-Jun-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110559
Kang TKim SYun HLee HJung K(2022)Gated Relational Encoder-Decoder Model for Target-Oriented Opinion Word ExtractionIEEE Access10.1109/ACCESS.2022.322883510(130507-130517)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3228835

Index Terms

Training Entire-Space Models for Target-oriented Opinion Words Extraction
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Sentiment analysis

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In this paper, we investigate two sub-tasks of aspect-based sentiment analysis (ABSA) through the pre-trained language model BERT, namely opinion target extraction (OTE) and target-oriented opinion words extraction (TOWE). Specifically, we build a novel ...
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Abstract
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Published In

cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
UNED
,
Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
Ben Carterette
Spotify
,
J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

Copyright © 2022 ACM.

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Published: 07 July 2022

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SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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Cited By

Huang JLi XDu YFan YHuang DChen X(2024)An aspect-opinion joint extraction model for target-oriented opinion words extraction on global spaceApplied Intelligence10.1007/s10489-024-05865-555:1Online publication date: 25-Nov-2024
https://doi.org/10.1007/s10489-024-05865-5
Wang PTao LTang MZhao MWang LXu YTian JMeng K(2023)A novel adaptive marker segmentation graph convolutional network for aspect-level sentiment analysisKnowledge-Based Systems10.1016/j.knosys.2023.110559270:COnline publication date: 21-Jun-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110559
Kang TKim SYun HLee HJung K(2022)Gated Relational Encoder-Decoder Model for Target-Oriented Opinion Word ExtractionIEEE Access10.1109/ACCESS.2022.322883510(130507-130517)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3228835

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