A hybrid deep neural network model for query intent classification

Article type: Research Article

Authors: Xu, Bo^{; *} | Ma, Yunlong | Lin, Hongfei^{; *}

Affiliations: Room A923 of Chuangxinyuan Building, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China

Correspondence: [*] Corresponding authors. Bo Xu and Hongfei Lin, Room A923 of Chuangxinyuan Building, Dalian University of Technology, Dalian, China. E-mails: xubo@dlut.edu.cn and hflin@dlut.edu.cn.

Abstract: 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 interpreted 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.

Keywords: Information retrieval, query intent classification, query representation, deep neural network model, machine learning

DOI: 10.3233/JIFS-182682

Journal: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 6413-6423, 2019