ACM SIGIR Forum
Query type classification for web document retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Engineering a multi-purpose test collection for web retrieval experiments
Information Processing and Management: an International Journal
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
The structure and performance of an open-domain question answering system
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Getting work done on the web: supporting transactional queries
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Navigating the intranet with high precision
Proceedings of the 16th international conference on World Wide Web
Exploring features for the automatic identification of user goals in web search
Information Processing and Management: an International Journal
Unsupervised transactional query classification based on webpage form understanding
Proceedings of the 20th ACM international conference on Information and knowledge management
Modeling transactional queries via templates
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Deriving query intents from web search engine queries
Journal of the American Society for Information Science and Technology
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User queries on the Web can be classified into three types according to user’s intention: informational query, navigational query and transactional query. In this paper, a query type classification method and Service Link information for transactional queries are proposed. Web mediated activity is usually implemented by hyperlinks. Hyperlinks can be good indicators in classifying queries and retrieving good answer pages for transactional queries. A hyperlink related to an anchor text has an anticipated action with a linked object. Possible actions are reading, visiting and downloading a linked object. We can assign a possible action to each anchor text. These tagged anchor texts can be used as training data for query type classification. We can collect a large-scale and dynamic train query set automatically. To see the accuracy of the proposing classification method, various experiments were conducted. From experiments, I could achieve 91% of possible improvement for transactional queries with our classification method.