Diversified Multiple Instance Learning for Document-Level Multi-Aspect Sentiment Classification
Yunjie Ji, Hao Liu, Bolei He, Xinyan Xiao, Hua Wu, Yanhua Yu
Abstract
Neural Document-level Multi-aspect Sentiment Classification (DMSC) usually requires a lot of manual aspect-level sentiment annotations, which is time-consuming and laborious. As document-level sentiment labeled data are widely available from online service, it is valuable to perform DMSC with such free document-level annotations. To this end, we propose a novel Diversified Multiple Instance Learning Network (D-MILN), which is able to achieve aspect-level sentiment classification with only document-level weak supervision. Specifically, we connect aspect-level and document-level sentiment by formulating this problem as multiple instance learning, providing a way to learn aspect-level classifier from the back propagation of document-level supervision. Two diversified regularizations are further introduced in order to avoid the overfitting on document-level signals during training. Diversified textual regularization encourages the classifier to select aspect-relevant snippets, and diversified sentimental regularization prevents the aspect-level sentiments from being overly consistent with document-level sentiment. Experimental results on TripAdvisor and BeerAdvocate datasets show that D-MILN remarkably outperforms recent weakly-supervised baselines, and is also comparable to the supervised method.- Anthology ID:
- 2020.emnlp-main.570
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7012–7023
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.570
- DOI:
- 10.18653/v1/2020.emnlp-main.570
- Cite (ACL):
- Yunjie Ji, Hao Liu, Bolei He, Xinyan Xiao, Hua Wu, and Yanhua Yu. 2020. Diversified Multiple Instance Learning for Document-Level Multi-Aspect Sentiment Classification. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7012–7023, Online. Association for Computational Linguistics.
- Cite (Informal):
- Diversified Multiple Instance Learning for Document-Level Multi-Aspect Sentiment Classification (Ji et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2020.emnlp-main.570.pdf