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Detecting intent in Web search activity is important task for finding relevant Web information. However extracting intents from users' queries is difficult as users express their intent by issuing short and often ambiguous queries, yet at the same time it is crucial factor for enhancing user satisfaction. Showing the variety of candidate intents behind a query could help users choose correct intent expressions and improve the Web search. In this paper, we propose the methodology for detecting intent of Web queries using Community Question-Answer (CQA)information. Our assumption is that questions and its answers in CQA corpus reflect intents of questioners. To detect these intents, we use the semantic connections between questions and its answers. We categorize questions to find the connections of features within a question and its answers, detect intent words in answers by calculating supports of concerned CQA contents, and cluster questions and their answers by these intent words. Experimental results show that the variety of Web query intents can be found with satisfactory performance.