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
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4283–4293
- Language:
- URL:
- https://aclanthology.org/2024.naacl-long.241
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/fix-volume-bibkeys/2024.naacl-long.241.pdf