Aspect Sentiment Triplet Extraction with Syntax-Semantics Graph Convolutional Network [PDF]
International Journal of Computational Intelligence SystemsIn the traditional task of aspect sentiment triplet extraction, existing approaches typically focus on either syntactic or semantic features independently, failing to leverage the complementary integration of these two types of information.
Jingyun Zhang +3 more
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A More Fine-Grained Aspect–Sentiment–Opinion Triplet Extraction Task
Mathematics, 2023 Sentiment analysis aims to systematically study affective states and subjective information in digital text through computational methods. Aspect Sentiment Triplet Extraction (ASTE), a subtask of sentiment analysis, aims to extract aspect term, sentiment
Yuncong Li, Fang Wang, Sheng-hua Zhong
doaj +2 more sources
Multi-feature Interaction for Aspect Sentiment Triplet Extraction [PDF]
Jisuanji kexue yu tansuoAspect sentiment triple extraction is one of the subtasks of aspect-level sentiment analysis, which aims to extract aspect terms, corresponding opinion terms and sentiment polarity in sentence.
CHEN Linying, LIU Jianhua, ZHENG Zhixiong, LIN Jie, XU Ge, SUN Shuihua
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Integration of Multi-Branch GCNs Enhancing Aspect Sentiment Triplet Extraction
Applied Sciences, 2023 Aspect Sentiment Triplet Extraction (ASTE) is a complex and challenging task in Natural Language Processing (NLP). It aims to extract the triplet of aspect term, opinion term, and their associated sentiment polarity, which is a more fine-grained study in
Xuefeng Shi +5 more
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FOAL: Fine-grained Contrastive Learning for Cross-domain Aspect Sentiment Triplet Extraction [PDF]
, 2023 Aspect Sentiment Triplet Extraction (ASTE) has achieved promising results while relying on sufficient annotation data in a specific domain. However, it is infeasible to annotate data for each individual domain.
Dai, Xinyu +3 more
core +2 more sources
Dual Encoder: Exploiting the Potential of Syntactic and Semantic for Aspect Sentiment Triplet Extraction [PDF]
Aspect Sentiment Triple Extraction (ASTE) is an emerging task in fine-grained sentiment analysis. Recent studies have employed Graph Neural Networks (GNN) to model the syntax-semantic relationships inherent in triplet elements.
Xu, Xiujuan, Zhao, Xiaowei, Zhou, Yong
core +2 more sources
Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction [PDF]
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2021 ACL 2021, long paper, main ...
Lu Xu, Yew Ken Chia, Lidong Bing
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Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction [PDF]
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 2021 Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from sentences, where each triplet includes an entity, its associated sentiment, and the opinion span explaining the reason for the sentiment.Most existing research addresses this problem in a multi-stage pipeline manner, which neglects the mutual information between such three ...
Zhexue Chen +4 more
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Position-Aware Tagging for Aspect Sentiment Triplet Extraction [PDF]
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020 15 pages, 10 figures, accepted by EMNLP ...
Lu Xu, Hao Li, Wei Lu, Lidong Bing
openalex +3 more sources
Aspect-Sentiment-Multiple-Opinion Triplet Extraction [PDF]
, 2021 NLPCC ...
Fang Wang +4 more
openalex +4 more sources