Coupling feature selection and machine learning methods for navigational query identification

It is important yet hard to identify navigational queries in Web search due to a lack of sufficient information in Web queries, which are typically very short. In this paper we study several machine learning methods, including naive Bayes model, maximum entropy model, support vector machine (SVM), and stochastic gradient boosting tree (SGBT), for navigational query identification in Web search. To boost the performance of these machine techniques, we exploit several feature selection methods and propose coupling feature selection with classification approaches to achieve the best performance. Different from most prior work that uses a small number of features, in this paper, we study the problem of identifying navigational queries with thousands of available features, extracted from major commercial search engine results, Web search user click data, query log, and the whole Web's relational content. A multi-level feature extraction system is constructed. Our results on real searc...

• Type: Conference
• Year: 2006
• Where: CIKM
• Authors: Yumao Lu, Fuchun Peng, Xin Li, Nawaaz Ahmed
• Keywords: CIKM 2006, Feature Selection, Feature Selection Methods, Information Management, Navigational Queries