Query classification by leveraging explicit concept information

A key task in query understanding is interpreting user intentions from the limited words that the user submitted to the search engines. Query classification (QC) has been widely studied for this purpose, which classifies queries into a set of target categories as user search intents. Query classification is an important as well as difficult problem in the field of information retrieval, since the queries are usually short in length, ambiguous and noisy. In this case, traditional “bag-of-words” based classification methods fail to achieve high accuracy in the task of QC. In this paper, we propose to mine explicit “Concept” information to help resolve this problem. Specifically, we first leverage existing knowledge bases to enrich the short query from the concept level. Then we discuss the usage of the mined concept information and propose a novel language model based query classification method which takes both words and concepts into consideration. Experimental results show that the mined concepts are very informative and effective to improve query classification.