Article
Version 1
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Syntax-driven Target Opinion Word Extraction
Version 1
: Received: 29 November 2023 / Approved: 4 December 2023 / Online: 5 December 2023 (04:39:35 CET)
How to cite: Terry, R.; Cress, P.; Ali, W. Syntax-driven Target Opinion Word Extraction. Preprints 2023, 2023120118. https://doi.org/10.20944/preprints202312.0118.v1 Terry, R.; Cress, P.; Ali, W. Syntax-driven Target Opinion Word Extraction. Preprints 2023, 2023120118. https://doi.org/10.20944/preprints202312.0118.v1
Abstract
The process of Targeted Opinion Word Extraction (TOWE), a critical component of aspect-based sentiment analysis (ABSA), revolves around identifying opinionated words linked to specific aspect-terms within sentences. Existing deep learning approaches, while effective, often overlook the syntactic structure of sentences, a factor that previous studies have identified as beneficial for TOWE. In this study, we introduce the Syntactic-Enhanced Deep Learning Model (SEDLM) that integrates syntactic structures into deep learning frameworks for TOWE. Our approach leverages syntax-driven opinion potential scores and syntactic inter-word connections, enhancing model performance. Additionally, we introduce an innovative regularization strategy aimed at distinguishing word representations in TOWE tasks. Our comprehensive analysis reveals that SEDLM sets new benchmarks in performance across multiple standard datasets.
Keywords
Aspect-based Sentiment Analysis; Syntax Knowledge
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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