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Span-Pair Interaction and Tagging for Dialogue-Level Aspect-Based Sentiment Quadruple Analysis

Published: 13 May 2024 Publication History

Abstract

The Dialogue-level Aspect-based Sentiment Quadruple analysis (DiaASQ) task has recently received attention in the Aspect-Based Sentiment Analysis (ABSA) field. It aims to extract(target, aspect, opinion, sentiment) quadruples from multi-turn and multi-party dialogues. Compared to previous ABSA tasks focusing on text such as sentences, the DiaASQ task involves more complex contextual information and corresponding relations between terms, as well as longer sequences. These characteristics challenge existing methods that struggle to model explicit span-level interactions or have high computational costs. In this paper, we propose a span-pair interaction and tagging method to solve these issues, which includes a novel Span-pair Tagging Scheme (STS) and a simple and efficient Multi-level Representation Model (MRM). STS simplifies the DiaASQ task to a span-pair tagging task and explicitly captures complete span-level semantics by tagging span pairs. MRM efficiently models the dialogue structure information and span-level interactions by constructing multi-level contextual representation. Besides, we train a span ranker to improve the running efficiency of MRM. Extensive experiments on multilingual datasets demonstrate that our method outperforms existing state-of-the-art methods.

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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

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  1. Span-Pair Interaction and Tagging for Dialogue-Level Aspect-Based Sentiment Quadruple Analysis

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      cover image ACM Conferences
      WWW '24: Proceedings of the ACM Web Conference 2024
      May 2024
      4826 pages
      ISBN:9798400701719
      DOI:10.1145/3589334
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      Published: 13 May 2024

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      1. aspect-based sentiment analysis
      2. dialogue scene
      3. natural language processing
      4. quadruple extraction

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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

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