TY - GEN
T1 - A Generative Model for Structured Sentiment Analysis
AU - Li, Yihui
AU - Zhang, Yice
AU - Yang, Yifan
AU - Xu, Ruifeng
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Structured Sentiment Analysis (SSA) aims to extract the complete sentiment structure from a given text. Existing approaches predominantly rely on the interactions of words to predict the relationships between sentiment elements. While these methods have shown effectiveness, they overlook the rich label semantics associated with SSA tasks and necessitate extensive task-specific designs. In order to address the above problems, we propose a generative framework for tackling the SSA task. We designed two templates to transform the SSA task into a text generation problem, which facilitate the training process by formulating the SSA task as a text generation problem. Through experiments conducted on three SSA datasets, we demonstrate that our proposed generative approach outperforms all existing methods, thereby highlighting the advantages of employing the generative model for SSA.
AB - Structured Sentiment Analysis (SSA) aims to extract the complete sentiment structure from a given text. Existing approaches predominantly rely on the interactions of words to predict the relationships between sentiment elements. While these methods have shown effectiveness, they overlook the rich label semantics associated with SSA tasks and necessitate extensive task-specific designs. In order to address the above problems, we propose a generative framework for tackling the SSA task. We designed two templates to transform the SSA task into a text generation problem, which facilitate the training process by formulating the SSA task as a text generation problem. Through experiments conducted on three SSA datasets, we demonstrate that our proposed generative approach outperforms all existing methods, thereby highlighting the advantages of employing the generative model for SSA.
KW - sequence-to-sequence modeling
KW - structured sentiment analysis
UR - https://www.scopus.com/pages/publications/85174572938
U2 - 10.1007/978-3-031-45140-9_3
DO - 10.1007/978-3-031-45140-9_3
M3 - 会议稿件
AN - SCOPUS:85174572938
SN - 9783031451393
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 28
EP - 38
BT - Artificial Intelligence and Mobile Services – AIMS 2023 - 12th International Conference, Held as Part of the Services Conference Federation, SCF 2023, Proceedings
A2 - Yang, Yujiu
A2 - Wang, Xiaohui
A2 - Zhang, Liang-Jie
PB - Springer Science and Business Media Deutschland GmbH
T2 - 12th International Conference on AI and Mobile Services, AIMS 2023
Y2 - 23 September 2023 through 26 September 2023
ER -