CNU System in NTCIR 11 IMine Task Global Semantic Expansion for Hierarchical Query Intent Identification
Understanding user intent is important for interactive and personalized information retrieval. For ambiguous queries, user intent space actually forms a hierarchical top down architecture: from senses to subtopics, rather than a flat structure. This paper presents the CNU system in NTCIR 11 IMine task. Our method constructs the hierarchical structure by exploiting global semantic representation and expansion. The highlights include: 1) We use word semantic vectors and propose a query dependent semantic composition for representing query aspect phrases. Our target is to alleviate the term mismatch and data sparseness problems, which shallow lexical matching and co occurrence based local semantics are ineffective to overcome. 2) We expand query subtopics by introducing new words according to global semantic relatedness and cluster these words for query sense induction. The evaluation results on and post NTCIR 11 show that our method could mine query subtopics and senses effectively.