Kyoungman Bae
ABSTRACT
This paper introduces a new question expanding method for question classification in cQA services. Input questions are mostly generated by a small size of text in the cQA services, and test inputs consist of only a question whereas training data do a pair of question and answer. Thus, the input questions cannot provide enough information for good classification in many cases. To solve this problem, we propose the question expanding method by pseudo relevant feedback and automatic answer generation. For pseudo relevant feedback, we first find relevant question-answer pairs related to an input question using the Indri search engine, and then top relevant words are chosen as expanded words. The automatic answer generation tries to create pseudo answers by adding question-related words using translation probabilities from questions to answers by Giza++. As a result, we obtain the significant improved performances when two approaches are effectively combined.
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Index Terms
- An Effective Question Expanding Method for Question Classification in cQA services
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