Elements of information theory
Elements of information theory
An Accelerated Chow and Liu Algorithm: Fitting Tree Distributions to High-Dimensional Sparse Data
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
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
Heads and tails: studies of web search with common and rare queries
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Determining the informational, navigational, and transactional intent of Web queries
Information Processing and Management: an International Journal
To personalize or not to personalize: modeling queries with variation in user intent
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Understanding the relationship between searchers' queries and information goals
Proceedings of the 17th ACM conference on Information and knowledge management
Challenges in building large-scale information retrieval systems: invited talk
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Characterizing the influence of domain expertise on web search behavior
Proceedings of the Second ACM International Conference on Web Search and Data Mining
A Web Search Analysis Considering the Intention behind Queries
LA-WEB '08 Proceedings of the 2008 Latin American Web Conference
Graphical Models, Exponential Families, and Variational Inference
Graphical Models, Exponential Families, and Variational Inference
Understanding user's query intent with wikipedia
Proceedings of the 18th international conference on World wide web
Classifying and Characterizing Query Intent
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Using word-sense disambiguation methods to classify web queries by intent
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
A large-deviation analysis for the maximum likelihood learning of tree structures
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 2
Efficient algorithms for ranking with SVMs
Information Retrieval
Automatic query type identification based on click through information
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
The intention behind web queries
SPIRE'06 Proceedings of the 13th international conference on String Processing and Information Retrieval
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Reading the correct history?: modeling temporal intention in resource sharing
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
Journal of Web Engineering
"Piaf" vs "Adele": classifying encyclopedic queries using automatically labeled training data
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
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The problem of identifying user intent has received considerable attention in recent years, particularly in the context of improving the search experience via query contextualization. Intent can be characterized by multiple dimensions, which are often not observed from query words alone. Accurate identification of Intent from query words remains a challenging problem primarily because it is extremely difficult to discover these dimensions. The problem is often significantly compounded due to lack of representative training sample. We present a generic, extensible framework for learning the multi-dimensional representation of user intent from the query words. The approach models the latent relationships between facets using tree structured distribution which leads to an efficient and convergent algorithm, FastQ, for identifying the multi-faceted intent of users based on just the query words. We also incorporated WordNet to extend the system capabilities to queries which contain words that do not appear in the training data. Empirical results show that FastQ yields accurate identification of intent when compared to a gold standard.