Query intent detection based on clustering of phrase embedding

Understanding ambiguous or multi-faceted search queries is essential for information retrieval. The task of identifying the major aspects or senses of queries can be viewed as detection of query intents, where the intents are represented as a number of clusters. So the challenging issue in this task is how to generate intent candidates and group them semantically. This paper explores the competence of lexical statistics and embedding method. First a novel term expansion algorithm is designed to sketch all possible intent candidates. Moreover, an efficient query intent generation model is proposed, which learns latent representations for intent candidates via embedding-based methods. And then vectorized intent candidates are clustered and detected as query intents. Experimental results, based on the NTCIR-12 IMine-2 corpus, show that query intent generation model via phrase embedding significantly outperforms the state-of-art clustering algorithms in query intent detection.