@inproceedings{huang-etal-2018-siamese,
title = "{S}iamese Network-Based Supervised Topic Modeling",
author = "Huang, Minghui and
Rao, Yanghui and
Liu, Yuwei and
Xie, Haoran and
Wang, Fu Lee",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1494/",
doi = "10.18653/v1/D18-1494",
pages = "4652--4662",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Siamese Network-Based Supervised Topic Modeling
%A Huang, Minghui
%A Rao, Yanghui
%A Liu, Yuwei
%A Xie, Haoran
%A Wang, Fu Lee
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F huang-etal-2018-siamese
%X 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.
%R 10.18653/v1/D18-1494
%U https://aclanthology.org/D18-1494/
%U https://doi.org/10.18653/v1/D18-1494
%P 4652-4662
Markdown (Informal)
[Siamese Network-Based Supervised Topic Modeling](https://aclanthology.org/D18-1494/) (Huang et al., EMNLP 2018)
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.