Learning Query Ambiguity Models by Using Search Logs

• Published in: Journal of computer science and technology Vol. 25; no. 4; pp. 728 - 738
• Main Author: 宋睿华 窦志成 洪小文 俞勇
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.07.2010; Springer Nature B.V
• Subjects: Algorithms; Ambiguity; Artificial Intelligence; Classifiers; College baseball; Computer Science; Customization; Data Structures and Information Theory; Dictionaries; Documents; Information Systems Applications (incl.Internet); Learning; Logs; Mathematical models; Queries; Query processing; Regular Paper; Searching; Software; Software Engineering; Support vector machines; Theory of Computation; User experience; User needs; Word sense disambiguation; 学习分类器; 搜索结果; 支持向量机; 文本分类器; 模糊查询; 模糊模型; 网络搜索; query classification; ambiguous query; log mining
• DOI: 10.1007/s11390-010-9360-y
• ISSN: 1000-9000

Identifying ambiguous queries is crucial to research on personalized Web search and search result diversity. Intuitively, query logs contain valuable information on how many intentions users have when issuing a query. However, previous work showed user clicks alone are misleading in judging a query as being ambiguous or not. In this paper, we address the problem of learning a query ambiguity model by using search logs. First, we propose enriching a query by mining the documents clicked by users and the relevant follow up queries in a session. Second, we use a text classifier to map the documents and the queries into predefined categories. Third, we propose extracting features from the processed data. Finally, we apply a state-of-the-art algorithm, Support Vector Machine （SVM）, to learn a query ambiguity classifier. Experimental results verify that the sole use of click based features or session based features perform worse than the previous work based on top retrieved documents. When we combine the two sets of features, our proposed approach achieves the best effectiveness, specifically 86% in terms of accuracy. It significantly improves the click based method by 5.6% and the session based method by 4.6%.