Clustering user queries of a search engine
Proceedings of the 10th international conference on World Wide Web
Probabilistic query expansion using query logs
Proceedings of the 11th international conference on World Wide Web
Semantic similarity between search engine queries using temporal correlation
WWW '05 Proceedings of the 14th 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
Pachinko allocation: DAG-structured mixture models of topic correlations
ICML '06 Proceedings of the 23rd international conference on Machine learning
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
Proceedings of the 16th international conference on World Wide Web
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Extracting semantic relations from query logs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Using the wisdom of the crowds for keyword generation
Proceedings of the 17th 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
Efficient multiple-click models in web search
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Understanding user's query intent with wikipedia
Proceedings of the 18th international conference on World wide web
Mining broad latent query aspects from search sessions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
PSkip: estimating relevance ranking quality from web search clickthrough data
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
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Learning search tasks in queries and web pages via graph regularization
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
When recommendation meets mobile: contextual and personalized recommendation on the go
Proceedings of the 13th international conference on Ubiquitous computing
Multi-view random walk framework for search task discovery from click-through log
Proceedings of the 20th ACM international conference on Information and knowledge management
Topic modeling for named entity queries
Proceedings of the 20th ACM international conference on Information and knowledge management
Automatically constructing concept hierarchies of health-related human goals
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
Structured query suggestion for specialization and parallel movement: effect on search behaviors
Proceedings of the 21st international conference on World Wide Web
Active objects: actions for entity-centric search
Proceedings of the 21st international conference on World Wide Web
Mining entity types from query logs via user intent modeling
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Role-explicit query identification and intent role annotation
Proceedings of the 21st ACM international conference on Information and knowledge management
Extraction and evaluation of candidate named entities in search engine queries
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Sponsored search ad selection by keyword structure analysis
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Extracting query facets from search results
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Place value: word position shifts vital to search dynamics
Proceedings of the 22nd international conference on World Wide Web companion
Analyzing linguistic structure of web search queries
Proceedings of the 22nd international conference on World Wide Web companion
Unsupervised identification of synonymous query intent templates for attribute intents
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Heterogeneous graph-based intent learning with queries, web pages and Wikipedia concepts
Proceedings of the 7th ACM international conference on Web search and data mining
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A significant portion of web search queries are name entity queries. The major search engines have been exploring various ways to provide better user experiences for name entity queries, such as showing "search tasks" (Bing search) and showing direct answers (Yahoo!, Kosmix). In order to provide the search tasks or direct answers that can satisfy most popular user intents, we need to capture these intents, together with relationships between them. In this paper we propose an approach for building a hierarchical taxonomy of the generic search intents for a class of name entities (e.g., musicians or cities). The proposed approach can find phrases representing generic intents from user queries, and organize these phrases into a tree, so that phrases indicating equivalent or similar meanings are on the same node, and the parent-child relationships of tree nodes represent the relationships between search intents and their sub-intents. Three different methods are proposed for tree building, which are based on directed maximum spanning tree, hierarchical agglomerative clustering, and pachinko allocation model. Our approaches are purely based on search logs, and do not utilize any existing taxonomies such as Wikipedia. With the evaluation by human judges (via Mechanical Turk), it is shown that our approaches can build trees of phrases that capture the relationships between important search intents.