Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Retrieval models for question and answer archives
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Hierarchical target type identification for entity-oriented queries
Proceedings of the 21st ACM international conference on Information and knowledge management
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Community Question Answering portals like Yahoo! Answers have recently become a popular method for seeking information online. Users express their information need as questions for which other users generate potential answers. These questions are organized into pre-defined hierarchical categories to facilitate effective answering, hence Question Classification is an important aspect of these systems. In this paper we propose a novel system, CQC, for automatically classifying new questions into one of the hierarchical categories. Experiments conducted on large scale real data from Yahoo Answers! show that the proposed techniques are effective and outperform existing methods significantly.