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This paper claims to use a new question expansion method for question classification in cQA services. The input questions consist of only a question whereas training data do a pair of question and answer. Thus they cannot provide enough information for good classification in many cases. Since the answer is strongly associated with the input questions, we try to create a pseudo answer to expand each input question. Translation probabilities between questions and answers and a pseudo relevant feedback technique are used to generate the pseudo answer. As a result, we obtain the significant improved performances when two approaches are effectively combined. key words: Question Classification, cQA Service, Pseudo Relevant Feedback (PRF), Question Expansion, Translation Probability.

How to Combine Translation Probabilities and Question Expansion for Question Classification in cQA Services