A hybrid deep neural network model for query intent classification

Query intents describe user information needs for searching on the web. How to capture the query intents is a crucial research topic in information retrieval. Search engine users always employ insufficient or unclear words as queries, thus making query intents ambiguous and uncertain to be interpre ted by search engines. Query intent classification can deal with the problem by clarifying user queries and interpreting information needs for improving user satisfaction. Two main challenges have been addressed to classify query intents: one is how to effectively represent short and ambiguous queries; the other is how to generate a set of appropriate categories for matching diverse queries. In the paper, we propose a hybrid deep neural network model for query intent classification to meet the challenges. Our model adopts two state-of-the-art neural network models to comprehensively represent queries as feature vectors. We then employ query logs to automatically generate intermediate categories for fine-grained query intent clarification. Experimental results show that our method can outperform other baseline models, and effectively improve the performance in query intent classification.