@inproceedings{yin-etal-2026-syntactic,
title = "Syntactic-Guided Semantic Attention Matrix Enhancement for Aspect Sentiment Triplet Extraction",
author = "Yin, Suhao and
Guo, Wenyu and
Yu, Dong and
Xue, Jian and
Zhangfengzhen and
Song, Yan and
Jin, Guoqing and
Lv, Ke",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.375/",
pages = "7916--7935",
ISBN = "979-8-89176-462-0",
abstract = "Aspect Sentiment Triplet Extraction (ASTE) is a fine-grained task in Aspect-Based Sentiment Analysis (ABSA), aiming to extract triplets of aspect terms, their corresponding opinion terms, and sentiment orientation. Recent studies have employed semantic and syntactic features to enhance extraction performance. However, as the extraction of both syntactic and semantic information is not task-specific, such information does not target aspect-opinion pairs within sentences, thereby potentially introducing noise unrelated to the task. To address this issue, we propose a Syntactic-Guided Semantic Attention Matrix Enhancement (SG-SAME) framework. First, we use a multi-view masking mechanism and a biaffine-based gating module to derive a task-aligned shared matrix from the existing semantic and syntactic matrices. Second, the shared matrix is injected into the attention score matrix of LLM layers to enhance the model{'}s representation learning. Finally, we propose a chain-of-thought (CoT) framework with negative candidate injection to steer the model toward autonomous reasoning and error self-rectification. Experimental results on multiple benchmark datasets demonstrate that our method outperforms existing state-of-the-art approaches in ASTE tasks. {Code will be available.}"
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<abstract>Aspect Sentiment Triplet Extraction (ASTE) is a fine-grained task in Aspect-Based Sentiment Analysis (ABSA), aiming to extract triplets of aspect terms, their corresponding opinion terms, and sentiment orientation. Recent studies have employed semantic and syntactic features to enhance extraction performance. However, as the extraction of both syntactic and semantic information is not task-specific, such information does not target aspect-opinion pairs within sentences, thereby potentially introducing noise unrelated to the task. To address this issue, we propose a Syntactic-Guided Semantic Attention Matrix Enhancement (SG-SAME) framework. First, we use a multi-view masking mechanism and a biaffine-based gating module to derive a task-aligned shared matrix from the existing semantic and syntactic matrices. Second, the shared matrix is injected into the attention score matrix of LLM layers to enhance the model’s representation learning. Finally, we propose a chain-of-thought (CoT) framework with negative candidate injection to steer the model toward autonomous reasoning and error self-rectification. Experimental results on multiple benchmark datasets demonstrate that our method outperforms existing state-of-the-art approaches in ASTE tasks. Code will be available.</abstract>
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%0 Conference Proceedings
%T Syntactic-Guided Semantic Attention Matrix Enhancement for Aspect Sentiment Triplet Extraction
%A Yin, Suhao
%A Guo, Wenyu
%A Yu, Dong
%A Xue, Jian
%A Song, Yan
%A Jin, Guoqing
%A Lv, Ke
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%A Zhangfengzhen
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F yin-etal-2026-syntactic
%X Aspect Sentiment Triplet Extraction (ASTE) is a fine-grained task in Aspect-Based Sentiment Analysis (ABSA), aiming to extract triplets of aspect terms, their corresponding opinion terms, and sentiment orientation. Recent studies have employed semantic and syntactic features to enhance extraction performance. However, as the extraction of both syntactic and semantic information is not task-specific, such information does not target aspect-opinion pairs within sentences, thereby potentially introducing noise unrelated to the task. To address this issue, we propose a Syntactic-Guided Semantic Attention Matrix Enhancement (SG-SAME) framework. First, we use a multi-view masking mechanism and a biaffine-based gating module to derive a task-aligned shared matrix from the existing semantic and syntactic matrices. Second, the shared matrix is injected into the attention score matrix of LLM layers to enhance the model’s representation learning. Finally, we propose a chain-of-thought (CoT) framework with negative candidate injection to steer the model toward autonomous reasoning and error self-rectification. Experimental results on multiple benchmark datasets demonstrate that our method outperforms existing state-of-the-art approaches in ASTE tasks. Code will be available.
%U https://aclanthology.org/2026.findings-emnlp.375/
%P 7916-7935
Markdown (Informal)
[Syntactic-Guided Semantic Attention Matrix Enhancement for Aspect Sentiment Triplet Extraction](https://aclanthology.org/2026.findings-emnlp.375/) (Yin et al., Findings 2026)
ACL
- Suhao Yin, Wenyu Guo, Dong Yu, Jian Xue, Zhangfengzhen, Yan Song, Guoqing Jin, and Ke Lv. 2026. Syntactic-Guided Semantic Attention Matrix Enhancement for Aspect Sentiment Triplet Extraction. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 7916–7935, Budapest, Hungary. Association for Computational Linguistics.