iACOS: Advancing Implicit Sentiment Extraction with Informative and Adaptive Negative Examples

Xiancai Xu, Jia-Dong Zhang, Lei Xiong, Zhishang Liu


Abstract
Aspect-based sentiment analysis (ABSA) have been extensively studied, but little light has been shed on the quadruple extraction consisting of four fundamental elements: aspects, categories, opinions and sentiments, especially with implicit aspects and opinions. In this paper, we propose a new method iACOS for extracting Implicit Aspects with Categories and Opinions with Sentiments. First, iACOS appends two implicit tokens at the end of a text to capture the context-aware representation of all tokens including implicit aspects and opinions. Second, iACOS develops a sequence labeling model over the context-aware token representation to co-extract explicit and implicit aspects and opinions. Third, iACOS devises a multi-label classifier with a specialized multi-head attention for discovering aspect-opinion pairs and predicting their categories and sentiments simultaneously. Fourth, iACOS leverages informative and adaptive negative examples to jointly train the multi-label classifier and the other two classifiers on categories and sentiments by multi-task learning. Finally, the experimental results show that iACOS significantly outperforms other quadruple extraction baselines according to the F1 score on two public benchmark datasets.
Anthology ID:
2024.naacl-long.241
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
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Publisher:
Association for Computational Linguistics
Note:
Pages:
4283–4293
Language:
URL:
https://aclanthology.org/2024.naacl-long.241
DOI:
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Cite (ACL):
Xiancai Xu, Jia-Dong Zhang, Lei Xiong, and Zhishang Liu. 2024. iACOS: Advancing Implicit Sentiment Extraction with Informative and Adaptive Negative Examples. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 4283–4293, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
iACOS: Advancing Implicit Sentiment Extraction with Informative and Adaptive Negative Examples (Xu et al., NAACL 2024)
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