Baolin Peng
ABSTRACT
One of the main research tasks in Community question answering (CQA) is to find most relevant questions for a given new query, thereby providing useful knowledge for the users. Traditionally used methods such as bag-of-words or latent semantic models consider queries, questions and answers in a same feature space. However, the correlations among queries, questions and answers imply that they lie in different feature spaces. In light of these issues, we proposed a tri-modal deep boltzmann machine (tri-DBM) to extract unified representation for query, question and answer. Experiments on Yahoo! Answers dataset reveal using these unified representation to train a classifier judging semantic matching level between query and question outperforms models using bag-of-words or LSA representation significantly.
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Digital Library
- J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng. Multimodal deep learning. In ICML, pages 689--696, 2011.Google ScholarDigital Library
- R. Salakhutdinov and G. E. Hinton. Replicated softmax: an undirected topic model. In NIPS, pages 1607--1614, 2009.Google ScholarDigital Library
Index Terms
- Learning joint representation for community question answering with tri-modal DBM
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