Document clustering with committees
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
ACM SIGIR Forum
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Automatically identifying localizable queries
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Named entity recognition in query
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Extracting structured information from user queries with semi-supervised conditional random fields
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
The linguistic structure of English web-search queries
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Learning question paraphrases for QA from Encarta logs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
What you seek is what you get: extraction of class attributes from query logs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Identifying synonyms among distributionally similar words
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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
Structured annotations of web queries
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Using search session context for named entity recognition in query
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Structural annotation of search queries using pseudo-relevance feedback
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Improving recommendation for long-tail queries via templates
Proceedings of the 20th international conference on World wide web
Proceedings of the 20th international conference on World wide web
Sequence clustering and labeling for unsupervised query intent discovery
Proceedings of the fifth ACM international conference on Web search and data mining
Active objects: actions for entity-centric search
Proceedings of the 21st international conference on World Wide Web
A framework for robust discovery of entity synonyms
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Among all web search queries there is an important subset of queries containing entity mentions. In these queries, it is observed that users are most interested in requesting some attribute of an entity, such as "Obama age" for the intent of age, which we refer to as the attribute intent. In this work we address the problem of identifying synonymous query intent templates for the attribute intent. For example, "how old is [Person]" and "[Person]'s age" are both synonymous templates for the age intent. Successful identification of the synonymous query intent templates not only can improve the performance of all existing query annotation approaches, but also could benefit applications such as instant answers and intent-based query suggestion. In this work we propose a clustering framework with multiple kernel functions to identify synonymous query intent templates for a set of canonical templates jointly. Furthermore, signals from multiple sources of information are integrated into a kernel function between templates, where the weights of these signals are tuned in an unsupervised manner. We have conducted extensive experiments across multiple domains in FreeBase, and results demonstrate the effectiveness of our clustering framework for finding synonymous query intent templates for attribute intents.