Identifying new user intents is an essential task in the dialogue system. However, it is hard to get satisfying clustering results since the definition of intents is strongly guided by prior knowledge. Existing methods incorporate prior knowledge by intensive feature engineering, which not only leads to overfitting but also makes it sensitive to the number of clusters. In this paper, we propose constrained deep adaptive clustering with cluster refinement (CDAC+), an end-to-end clustering method that can naturally incorporate pairwise constraints as prior knowledge to guide the clustering process. Moreover, we refine the clusters by forcing the model to learn from the high confidence assignments. After eliminating low confidence assignments, our approach is surprisingly insensitive to the number of clusters. Experimental results on the three benchmark datasets show that our method can yield significant improvements over strong baselines. 1
The organizers of ACM Seventh International Workshop on Data and Text Mining in Biomedical Informatics (DTMBIO 13) are pleased to announce that the seventh DTMBIO will be held in conjunction with CIKM, one of the largest data management conferences. The major interests of DTMBIO are on the state-ofthe-art applications of data and text mining on biomedical research problems. DTMBIO 13 will be a forum of discussing and exchanging informatics related techniques and problems in the context of biomedical research.
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