Natural language understanding (2nd ed.)
Natural language understanding (2nd ed.)
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Using part-of-speech patterns to reduce query ambiguity
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
On the complexity of ID/LP parsing 1
Computational Linguistics
Optimizing web search using web click-through data
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Discriminative Reranking for Natural Language Parsing
Computational Linguistics
Unsupervised learning of field segmentation models for information extraction
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Robust classification of rare queries using web knowledge
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
The role of documents vs. queries in extracting class attributes from text
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Learning query intent from regularized click graphs
Proceedings of the 31st annual 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
A machine learning approach to building domain-specific search engines
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Understanding the semantic structure of noun phrase queries
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Structural annotation of search queries using pseudo-relevance feedback
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Joint annotation of search queries
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Labeling queries for a people search engine
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
Using search-logs to improve query tagging
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Interpreting keyword queries over web knowledge bases
Proceedings of the 21st ACM international conference on Information and knowledge management
Hierarchical target type identification for entity-oriented queries
Proceedings of the 21st ACM international conference on Information and knowledge management
Mining subtopics from different aspects for diversifying search results
Information Retrieval
Crowdsourcing-assisted query structure interpretation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We present a novel approach to parse web search queries for the purpose of automatic tagging of the queries. We will define a set of probabilistic context-free rules, which generates bags (i.e. multi-sets) of words. Using this new type of rule in combination with the traditional probabilistic phrase structure rules, we define a hybrid grammar, which treats each search query as a bag of chunks (i.e. phrases). A hybrid probabilistic parser is used to parse the queries. In order to take contextual information into account, a discriminative model is used on top of the parser to re-rank the n-best parse trees generated by the parser. Experiments show that our approach outperforms a basic model, which is based on Conditional Random Fields.