Abstract
Query completion has long been proved useful to help a user explore and express his information need. In general search, such completions can be generated from a large scale query log and other accessory information. However, without query log, how to generate query completion for community-based Question Answering (cQA) search remains a challenging problem. In this work, we propose a novel query completion algorithm based on ranking cQA questions with entity and phrase information for cQA search, and a demonstration system has been developed. Without involvement of query log, this method clearly helps users complete their queries. Empirical experiments on a large scale cQA dataset show that the proposed algorithm can successfully improve user experience.
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