Bridging the lexical chasm: statistical approaches to answer-finding
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Chinese word segmentation based on maximum matching and word binding force
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
New Directions in Question Answering
New Directions in Question Answering
Finding similar questions in large question and answer archives
Proceedings of the 14th ACM international conference on Information and knowledge management
Mining Domain-Specific Thesauri from Wikipedia: A Case Study
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Enhancing text clustering by leveraging Wikipedia semantics
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Retrieval models for question and answer archives
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Building semantic kernels for text classification using wikipedia
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving Text Classification by Using Encyclopedia Knowledge
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Exploiting Wikipedia as external knowledge for document clustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Deriving a large scale taxonomy from Wikipedia
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
The use of categorization information in language models for question retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
Exploiting internal and external semantics for the clustering of short texts using world knowledge
Proceedings of the 18th ACM conference on Information and knowledge management
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Proceedings of the 19th international conference on World wide web
Modeling semantic relevance for question-answer pairs in web social communities
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Phrase-based translation model for question retrieval in community question answer archives
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Large-scale question classification in cQA by leveraging Wikipedia semantic knowledge
Proceedings of the 20th ACM international conference on Information and knowledge management
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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Community question answering (cQA), which provides a platform for people with diverse background to share information and knowledge, has become an increasingly popular research topic. In this paper, we focus on the task of question retrieval. The key problem of question retrieval is to measure the similarity between the queried questions and the historical questions which have been solved by other users. The traditional methods measure the similarity based on the bag-of-words (BOWs) representation. This representation neither captures dependencies between related words, nor handles synonyms or polysemous words. In this work, we first propose a way to build a concept thesaurus based on the semantic relations extracted from the world knowledge of Wikipedia. Then, we develop a unified framework to leverage these semantic relations in order to enhance the question similarity in the concept space. Experiments conducted on a real cQA data set show that with the help of Wikipedia thesaurus, the performance of question retrieval is improved as compared to the traditional methods.