TY - GEN
T1 - Simple Approach for Aspect Sentiment Triplet Extraction Using Span-Based Segment Tagging and Dual Extractors
AU - Li, Dongxu
AU - Yang, Zhihao
AU - Lan, Yuquan
AU - Zhang, Yunqi
AU - Zhao, Hui
AU - Zhao, Gang
N1 - Publisher Copyright:
© 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2023/7/18
Y1 - 2023/7/18
N2 - Aspect sentiment triplet extraction (ASTE) is a task which extracts aspect terms, opinion terms, and sentiment polarities as triplets from review sentences. Existing approaches have developed bidirectional structures for term interaction. Sentiment polarities are subsequently extracted from aspect-opinion pairs. These solutions suffer from: 1) high dependency on custom bidirectional structures, 2) inadequate representation of the information through existing tagging schemes, and 3) insufficient usage of all available sentiment data. To address the above issues, we propose a simple span-based solution named SimSTAR with Segment Tagging And dual extRactors. SimSTAR does not introduce any additional bidirectional mechanism. The segment tagging scheme is capable to indicate all possible cases of spans and reveals more information through negative labels. Dual extractors are employed to make the sentiment extraction independent of the term extraction. We evaluate our model on four ASTE datasets. The experimental results show that our simple method achieves state-of-the-art performance.
AB - Aspect sentiment triplet extraction (ASTE) is a task which extracts aspect terms, opinion terms, and sentiment polarities as triplets from review sentences. Existing approaches have developed bidirectional structures for term interaction. Sentiment polarities are subsequently extracted from aspect-opinion pairs. These solutions suffer from: 1) high dependency on custom bidirectional structures, 2) inadequate representation of the information through existing tagging schemes, and 3) insufficient usage of all available sentiment data. To address the above issues, we propose a simple span-based solution named SimSTAR with Segment Tagging And dual extRactors. SimSTAR does not introduce any additional bidirectional mechanism. The segment tagging scheme is capable to indicate all possible cases of spans and reveals more information through negative labels. Dual extractors are employed to make the sentiment extraction independent of the term extraction. We evaluate our model on four ASTE datasets. The experimental results show that our simple method achieves state-of-the-art performance.
KW - Aspect Sentiment Triplet Extraction
KW - Dual Extractors
KW - Segment Tagging Scheme
KW - Span-based
UR - https://www.scopus.com/pages/publications/85168655494
U2 - 10.1145/3539618.3592060
DO - 10.1145/3539618.3592060
M3 - 会议稿件
AN - SCOPUS:85168655494
T3 - SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
SP - 2374
EP - 2378
BT - SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
PB - Association for Computing Machinery, Inc
T2 - 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023
Y2 - 23 July 2023 through 27 July 2023
ER -