Siamese Network-Based Supervised Topic Modeling
Minghui Huang, Yanghui Rao, Yuwei Liu, Haoran Xie, Fu Lee Wang
Abstract
Label-specific topics can be widely used for supporting personality psychology, aspect-level sentiment analysis, and cross-domain sentiment classification. To generate label-specific topics, several supervised topic models which adopt likelihood-driven objective functions have been proposed. However, it is hard for them to get a precise estimation on both topic discovery and supervised learning. In this study, we propose a supervised topic model based on the Siamese network, which can trade off label-specific word distributions with document-specific label distributions in a uniform framework. Experiments on real-world datasets validate that our model performs competitive in topic discovery quantitatively and qualitatively. Furthermore, the proposed model can effectively predict categorical or real-valued labels for new documents by generating word embeddings from a label-specific topical space.- Anthology ID:
- D18-1494
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4652–4662
- Language:
- URL:
- https://aclanthology.org/D18-1494
- DOI:
- 10.18653/v1/D18-1494
- Cite (ACL):
- Minghui Huang, Yanghui Rao, Yuwei Liu, Haoran Xie, and Fu Lee Wang. 2018. Siamese Network-Based Supervised Topic Modeling. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4652–4662, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Siamese Network-Based Supervised Topic Modeling (Huang et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/D18-1494.pdf
- Data
- ISEAR