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Improving context-aware query classification via adaptive self-training
2011
Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM '11
Topical classification of user queries is critical for generalpurpose web search systems. It is also a challenging task, due to the sparsity of query terms and the lack of labeled queries. On the other hand, search contexts embedded in query sessions and unlabeled queries free on the web have not been fully utilized in most query classification systems. In this work, we leverage these information to improve query classification accuracy. We first incorporate search contexts into our framework
doi:10.1145/2063576.2063598
dblp:conf/cikm/ChenSNC11
fatcat:6yvgsjbxcbf57jbkml4eq43ary