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IAOTP: An Interactive End-to-End Solution for Aspect-Opinion Term Pairs Extraction

Published: 07 July 2022 Publication History

Abstract

Recently, the aspect-opinion term pairs (AOTP) extraction task has gained substantial importance in the domain of aspect-based sentiment analysis. It intends to extract the potential pair of each aspect term with its corresponding opinion term present in a user review. Some existing studies heavily relied on the annotated aspect terms and/or opinion terms, or adopted external knowledge/resources to figure out the task. Therefore, in this study, we propose a novel end-to-end solution, called an Interactive AOTP (IAOTP) model, for exploring AOTP. The IAOTP model first tracks the boundary of each token in given aspect-specific and opinion-specific representations through a span-based operation. Next, it generates the candidate AOTP by formulating the dyadic relations between tokens through the Biaffine transformation. Then, it computes the positioning information to capture the significant distance relationship that each candidate pair holds. And finally, it jointly models collaborative interactions and prediction of AOTP through a 2D self-attention. Besides the IAOTP model, this study also proposes an independent aspect/opinion encoding model (a RS model) that formulates relational semantics to obtain aspect-specific and opinion-specific representations that can effectively perform the extraction of aspect and opinion terms. Detailed experiments conducted on the publicly available benchmark datasets for AOTP, aspect terms, and opinion terms extraction tasks, clearly demonstrate the significantly improved performance of our models relative to other competitive state-of-the-art baselines.

Supplementary Material

MP4 File (SIGIR22-fp1010.mp4)
A brief presentation of our model "IAOTP: An Interactive End-to-End Solution for Aspect-Opinion Term Pairs Extraction "

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Cited By

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  • (2024)A systematic review of aspect-based sentiment analysis: domains, methods, and trendsArtificial Intelligence Review10.1007/s10462-024-10906-z57:11Online publication date: 17-Sep-2024
  • (2023)Enhancing aspect-based sentiment analysis with dependency-attention GCN and mutual assistance mechanismJournal of Intelligent Information Systems10.1007/s10844-023-00811-262:1(163-189)Online publication date: 1-Sep-2023

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  1. IAOTP: An Interactive End-to-End Solution for Aspect-Opinion Term Pairs Extraction

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    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
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 07 July 2022

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    Author Tags

    1. aspect-based sentiment analysis
    2. aspect-opinion term pairs extraction
    3. natural language processing

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    • National key research and development program in China
    • Shenzhen Science and Technology Project
    • Ministry of Education Fund Projects
    • the World-Class Universities (Disciplines) and the Characteristic Development Guidance Funds for the Central Universities
    • Basic Scientific Research Operating Expenses of Central Universities

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    View all
    • (2024)A systematic review of aspect-based sentiment analysis: domains, methods, and trendsArtificial Intelligence Review10.1007/s10462-024-10906-z57:11Online publication date: 17-Sep-2024
    • (2023)Enhancing aspect-based sentiment analysis with dependency-attention GCN and mutual assistance mechanismJournal of Intelligent Information Systems10.1007/s10844-023-00811-262:1(163-189)Online publication date: 1-Sep-2023

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