Probabilistic query expansion using query logs
Proceedings of the 11th international conference on World Wide Web
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
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
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Automatic identification of user goals in Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
A personalized search engine based on web-snippet hierarchical clustering
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Building bridges for web query classification
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Identifying User Goals from Web Search Results
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Robust classification of rare queries using web knowledge
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Improving Identification of Latent User Goals through Search-Result Snippet Classification
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Dependency parsing based on dynamic local optimization
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
The intention behind web queries
SPIRE'06 Proceedings of the 13th international conference on String Processing and Information Retrieval
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Understanding user goal has played an important role in improving the quality of the search engines. Many previous researches focus on finding prominent statistical features to classify the user goal into Border's taxonomy. But it is difficult to achieve high precision because of the weakness of taxonomy definition. This paper first gives a lexicalized taxonomy for user goal, and then proposes a dependency relation based algorithm to detect lexicalized user goals. To alleviate the sparseness of direct dependency relation, we extend our algorithm to include second order dependency relations. The experimental results show that our extended algorithm can achieve precision of 89% on correctness and 79% on relevance, and thus it outperforms previous related algorithm significantly.