Fundamentals of speech recognition
Fundamentals of speech recognition
ACM Computing Surveys (CSUR)
Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering user queries of a search engine
Proceedings of the 10th international conference on World Wide Web
Enriching web taxonomies through subject categorization of query terms from search engine logs
Decision Support Systems - Web retrieval and mining
A Scalable Algorithm for Clustering Sequential Data
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Ontologizing semantic relations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Semantic taxonomy induction from heterogenous evidence
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Improving search engines by query clustering
Journal of the American Society for Information Science and Technology
Introduction to Information Retrieval
Introduction to 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
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Entity extraction via ensemble semantics
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Towards rich query interpretation: walking back and forth for mining query templates
Proceedings of the 19th international conference on World wide web
Clustering query refinements by user intent
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
Understanding the semantic structure of noun phrase queries
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Improving recommendation for long-tail queries via templates
Proceedings of the 20th international conference on World wide web
Aid to regional development agencies: finding and matching research funding opportunities
Proceedings of the 13th Annual International Conference on Digital Government Research
Unsupervised identification of synonymous query intent templates for attribute intents
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Crowdsourcing-assisted query structure interpretation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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One popular form of semantic search observed in several modern search engines is to recognize query patterns that trigger instant answers or domain-specific search, producing semantically enriched search results. This often requires understanding the query intent in addition to the meaning of the query terms in order to access structured data sources. A major challenge in intent understanding is to construct a domain-dependent schema and to annotate search queries based on such a schema, a process that to date has required much manual annotation effort. We present an unsupervised method for clustering queries with similar intent and for producing a pattern consisting of a sequence of semantic concepts and/or lexical items for each intent. Furthermore, we leverage the discovered intent patterns to automatically annotate a large number of queries beyond those used in clustering. We evaluated our method on 10 selected domains, discovering over 1400 intent patterns and automatically annotating 125K (and potentially many more) queries. We found that over 90% of patterns and 80% of instance annotations tested are judged to be correct by a majority of annotators.