A Clicked-URL Feature for Transactional Query Identification
Yingcheng Sun, Kenneth A. Loparo · 2019
Understanding query intents can help search engines to effectively improve their search quality. Click-through data has proven to be a valuable resource for query classification. In this paper, we propose a novel Clicked-URL (CURL) feature that uses semantic information extracted from user clicked URLs in the search results, and compare with the "key word list" to identify transnational query type. Experiments show that we can obtain relatively high accuracy in transnational query identification with CURL, and achieve an improved performance in query classification combing with other features.