Text classification using string kernels
The Journal of Machine Learning Research
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Retrieving answers from frequently asked questions pages on the web
Proceedings of the 14th ACM international conference on Information and knowledge management
Fast and space efficient string kernels using suffix arrays
ICML '06 Proceedings of the 23rd international conference on Machine learning
Extracting relations with integrated information using kernel methods
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Retrieval models for question and answer archives
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Kernel methods, syntax and semantics for relational text categorization
Proceedings of the 17th ACM conference on Information and knowledge management
Ranking community answers by modeling question-answer relationships via analogical reasoning
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A syntactic tree matching approach to finding similar questions in community-based qa services
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
The use of categorization information in language models for question retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
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Finding similar questions in Community Question Answering (CQA) services plays more and more important role in current web and IR applications. The task aims to retrieve historical questions that are similar or relevant to new questions posed by users. However, traditional "bag-of-words" based models would fail to measure the similarity between question sentences, as they usually ignore sequential and syntactic information. In this paper, we propose a novel composite kernel to improve the accuracy in question matching. Our study illustrate that the composite kernel can efficiently capture both lexical semantics and syntactic information in a question sentence by leveraging word sequence kernel, POS tag sequence kernel and syntactic tree kernel. Experimental results on real world datasets show that our proposed method significantly outperforms the state-of-the-art models.