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Revisiting Structured Sentiment Analysis as Latent Dependency Graph Parsing

Chengjie Zhou, Bobo Li, Hao Fei, Fei Li, Chong Teng, Donghong Ji


Abstract
Structured Sentiment Analysis (SSA) was cast as a problem of bi-lexical dependency graph parsing by prior studies.Multiple formulations have been proposed to construct the graph, which share several intrinsic drawbacks:(1) The internal structures of spans are neglected, thus only the boundary tokens of spans are used for relation prediction and span recognition, thus hindering the model’s expressiveness;(2) Long spans occupy a significant proportion in the SSA datasets, which further exacerbates the problem of internal structure neglect.In this paper, we treat the SSA task as a dependency parsing task on partially-observed dependency trees, regarding flat spans without determined tree annotations as latent subtrees to consider internal structures of spans.We propose a two-stage parsing method and leverage TreeCRFs with a novel constrained inside algorithm to model latent structures explicitly, which also takes advantages of joint scoring graph arcs and headed spans for global optimization and inference. Results of extensive experiments on five benchmark datasets reveal that our method performs significantly better than all previous bi-lexical methods, achieving new state-of-the-art.
Anthology ID:
2024.acl-long.548
Volume:
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10178–10191
Language:
URL:
https://aclanthology.org/2024.acl-long.548
DOI:
10.18653/v1/2024.acl-long.548
Bibkey:
Cite (ACL):
Chengjie Zhou, Bobo Li, Hao Fei, Fei Li, Chong Teng, and Donghong Ji. 2024. Revisiting Structured Sentiment Analysis as Latent Dependency Graph Parsing. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10178–10191, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Revisiting Structured Sentiment Analysis as Latent Dependency Graph Parsing (Zhou et al., ACL 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.acl-long.548.pdf