Yuchen Liu, Xiaochuan Ni, Jian-Tao Sun, and Zheng Chen
Query type classification aims to classify search queries into categories like navigational, informational and transactional, etc., according to the type of information need behind the queries. Although this problem has drawn many research attentions, previous methods usually require editors to label queries as training data or need domain knowledge to edit rules for predicting query type. Also, the existing work has been mainly focusing on the classification of informational and navigational query types. Transactional query classification has not been well addressed. In this work, we propose an unsupervised approach for transactional query classification. This method is based on the observation that, after the transactional queries are issued to a search engine, many users will click the search result pages and then have interactions with Web forms on these pages. The interactions, e.g., typing in text box, making selections from dropdown list, clicking on a button to execute actions, are used to specify detailed information of the transaction. By mining toolbar search log data, which records the associations between queries and Web forms clicked by users, we can get a set of good quality transactional queries without using manual labeling efforts. By matching these automatically acquired transactional queries and their associated Web form contents, we can generalize these queries into patterns. These patterns can be used to classify queries which are not covered by search log. Our experiments indicate that transactional queries produced by this method have good quality. The pattern based classifier achieves 83% F1 classification result. This is very effective considering the fact that we do not adopt any labeling efforts to train the classifier.
In 20th ACM Conference on Information and Knowledge Management (CIKM)