Abstract
Machine learning-based methods have obtained great progress on emotion classification. However, in most previous studies, the models are learned based on a single corpus which often suffers from insufficient labeled data. In this paper, we propose a corpus fusion approach to address emotion classification across two corpora which use different emotion taxonomies. The objective of this approach is to utilize the annotated data from one corpus to help the emotion classification on another corpus. An Integer Linear Programming (ILP) optimization is proposed to refine the classification results. Empirical studies show the effectiveness of the proposed approach to corpus fusion for emotion classification.- Anthology ID:
- C16-1310
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 3287–3297
- Language:
- URL:
- https://aclanthology.org/C16-1310
- DOI:
- Cite (ACL):
- Suyang Zhu, Shoushan Li, Ying Chen, and Guodong Zhou. 2016. Corpus Fusion for Emotion Classification. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3287–3297, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Corpus Fusion for Emotion Classification (Zhu et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/C16-1310.pdf