Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Aspect sentiment triplet extraction (ASTE), which aims to identify aspects from review sentences along with their corresponding opinion expressions and sentiments, is an emerging task in fine-grained opinion mining. Since ASTE consists of multiple subtasks, including opinion entity extraction, relation detection, and sentiment classification, it is ...
Shaowei Chen +3 more
openalex +4 more sources
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
A Span-level Bidirectional Network for Aspect Sentiment Triplet Extraction [PDF]
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022 Aspect Sentiment Triplet Extraction (ASTE) is a new fine-grained sentiment analysis task that aims to extract triplets of aspect terms, sentiments, and opinion terms from review sentences. Recently, span-level models achieve gratifying results on ASTE task by taking advantage of the predictions of all possible spans.
Yuqi Chen +3 more
openalex +3 more sources
ASTE-Transformer: Modelling Dependencies in Aspect-Sentiment Triplet Extraction [PDF]
Findings of the Association for Computational Linguistics: EMNLP 2024Aspect-Sentiment Triplet Extraction (ASTE) is a recently proposed task of aspect-based sentiment analysis that consists in extracting (aspect phrase, opinion phrase, sentiment polarity) triples from a given sentence. Recent state-of-the-art methods approach this task by first extracting all possible text spans from a given text, then filtering the ...
Iwo Naglik, Mateusz Lango
openalex +3 more sources
Target-to-Source Augmentation for Aspect Sentiment Triplet Extraction [PDF]
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023 Yice Zhang +5 more
openalex +2 more sources
Aspect Sentiment Triplet Extraction Using Reinforcement Learning [PDF]
Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021 CIKM ...
Samson Yu Bai Jian +3 more
openaire +2 more sources
Learning Cooperative Interactions for Multi-Overlap Aspect Sentiment Triplet Extraction [PDF]
Findings of the Association for Computational Linguistics: EMNLP 2022, 2022 Shiman Zhao, Wei Chen, Tengjiao Wang
openalex +2 more sources
Double Policy Network for Aspect Sentiment Triplet Extraction (Student Abstract)
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 Aspect Sentiment Triplet Extraction (ASTE) is the task to extract aspects, opinions and associated sentiments from sentences. Previous studies do not adequately consider the complicated interactions between aspect and opinion terms in both extraction logic and strategy. We present a novel Double Policy Network with Multi-Tag based Reward model (DPN-MTR)
Xuting Li +4 more
openalex +3 more sources
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
doaj +1 more source