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Searching semantically similar questions from a large community-based question archive
2009 International Conference on Natural Language Processing and Knowledge Engineering
This paper provides a novel and totally statistical method to search similar questions from a large question archive for a given queried question. Firstly, a word relevance model is trained based on the whole question archive which is made up of millions of natural language questions proposed by users on the web. The word relevance model is utilized to find most semantically related words to a specific word. Secondly, in order to find semantically similar questions for a queried question, eachdoi:10.1109/nlpke.2009.5313808 dblp:conf/nlpke/LiuLY09 fatcat:dmnwrcrrorhubmxfprsvpzjpoe