实验室一项研究成果被期刊Journal of Intelligent & Fuzzy Systems录用
新闻来源:IR实验室       发布时间:2019/2/20 20:47:21

  近日, 收到期刊《Journal of Intelligent & Fuzzy Systems》编辑部邮件,实验室徐博等的研究工作“A Hybrid Deep Neural Network Model for Query Intent Classification”已被录用。

  摘要:查询意图用以描述用户在互联网搜索时的信息需求,如何准确挖掘查询意图是信息检索领域的重要研究课题。而搜索引擎用户提交的查询往往很难准确刻画查询意图,进而导致其信息需求的模糊性和不确定性,为搜索引擎优化带来巨大难题。为解决该难题,本文着眼于查询意图挖掘任务,以澄清用户信息需求,提升用户搜索体验。具体地,我们从查询向量表示和查询类别匹配两个方面进行了优化,在查询向量表示方面,采用深度神经网络模型将查询表示为向量;在查询类别匹配方面,基于查询日志和开放式目录自动生成查询中间类别,最终实现查询意图分类。实验结果验证了所提出方法在查询意图挖掘方面的有效性。

  AbstractQuery 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.