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Eng."],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Aspect sentiment triplet extraction (ASTE), which aims to extract aspect terms, opinion terms, and sentiment polarity from textual comments, is a crucial task in aspect-based sentiment analysis. Most existing approaches focus on leveraging contextual information while neglecting the effective utilization of syntactic structures within the text. To improve extraction performance, this paper proposes a syntax-enhanced multi-task learning model, SE-ASTE, which jointly extracts aspect sentiment triplets by incorporating syntax connections and dependency edge type information. Specifically, the ASTE task is decomposed into three sub-tasks: opinion entity extraction, relation detection, and sentiment extraction. To capture syntactic dependencies, we employ a graph convolutional network with an attention mechanism, which computes the importance of dependency edges and aggregates node information in a targeted manner to generate a syntax-enhanced contextual representation. Subsequently, a self-attention module is utilized to generate task-specific features, while a sentiment extraction module, based on a affine scorer, captures sentiment relationships between words. Experimental results on the ASTE-Data-V2 dataset demonstrate that SE-ASTE achieves an average improvement of 1.45% in\u00a0the F1-score compared to baseline models, highlighting its effectiveness in aspect sentiment triplet extraction.<\/jats:p>","DOI":"10.1007\/s41019-025-00289-8","type":"journal-article","created":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T04:51:29Z","timestamp":1747630289000},"page":"515-531","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Syntactic-Enhanced Multi-Task Learning Model for Aspect Sentiment Triplet Extraction"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3152-1760","authenticated-orcid":false,"given":"Jiaxing","family":"Shang","sequence":"first","affiliation":[]},{"given":"Yuxuan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Linyang","family":"Zhong","sequence":"additional","affiliation":[]},{"given":"Ruiyuan","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,19]]},"reference":[{"issue":"2","key":"289_CR1","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1109\/TAFFC.2020.2970399","volume":"13","author":"A Nazir","year":"2020","unstructured":"Nazir A, Rao Y, Wu L, Sun L (2020) Issues and challenges of aspect-based sentiment analysis: a comprehensive survey. 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