ACM SIGIR Forum
The Journal of Machine Learning Research
Categorizing web queries according to geographical locality
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Automatic web query classification using labeled and unlabeled training data
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Q2C@UST: our winning solution to query classification in KDDCUP 2005
ACM SIGKDD Explorations Newsletter
Building bridges for web query classification
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Robust classification of rare queries using web knowledge
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Query classification based on index association rule expansion
WISM'11 Proceedings of the 2011 international conference on Web information systems and mining - Volume Part II
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This paper addresses the problem of query classification (QC), which aims to classify Web search queries into one or more predefined categories. The state-of-the-art solution for QC is to employ a bridging classifier via an intermediate taxonomy. In this paper, we advanced the bridging method by leveraging probabilistic topic models. The topic model, referred as RCTM (Regularized Correlated Topic Model), is an extension of the conventional CTM (Correlated Topic Model). RCTM learns a topic model by leveraging weak supervision from existing annotated data rather than in an unsupervised fashion, and thus it can effectively address the problem in topic modeling while the topics are predefined. The experimental evaluations show that our QC approach outperforms other baseline methods.