Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
The Journal of Machine Learning Research
Identifying similarities, periodicities and bursts for online search queries
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Semantic similarity between search engine queries using temporal correlation
WWW '05 Proceedings of the 14th international conference on World Wide Web
Context-aware query suggestion by mining click-through and session data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
The query-flow graph: model and applications
Proceedings of the 17th ACM conference on Information and knowledge management
Mining broad latent query aspects from search sessions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Named entity recognition in query
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Automatic generation of topic pages using query-based aspect models
Proceedings of the 18th ACM conference on Information and knowledge management
Clustering query refinements by user intent
Proceedings of the 19th international conference on World wide web
Building taxonomy of web search intents for name entity queries
Proceedings of the 19th international conference on World wide web
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Extracting query facets from search results
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Named entities are observed in a large portion of web search queries (named entity queries), where each entity can be associated with many different query terms that refer to various aspects of this entity. Organizing these query terms into topics helps understand major search intents about entities and the discovered topics are useful for applications such as query suggestion. Furthermore, we notice that named entities can often be organized into categories and those from the same category share many generic topics. Therefore, working on a category of named entities instead of individual ones helps avoid the problems caused by the sparsity and noise in the data. In this paper, Named Entity Topic Model (NETM) is proposed to discover generic topics for a category of named entities, where the quality of the generic topics is improved through the model design and the parameter initialization. Experiments based on query log data show that NETM discovers high-quality topics and outperforms the state-of-the-art techniques by 12.8% based on F1 measure.