SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Using temporal profiles of queries for precision prediction
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A framework for selective query expansion
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Overview of patent retrieval task at NTCIR-3
PATENT '03 Proceedings of the ACL-2003 workshop on Patent corpus processing - Volume 20
Term distillation in patent retrieval
PATENT '03 Proceedings of the ACL-2003 workshop on Patent corpus processing - Volume 20
Combining fields for query expansion and adaptive query expansion
Information Processing and Management: an International Journal
Towards robust query expansion: model selection in the language modeling framework
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Enhancing patent retrieval by citation analysis
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Incorporating term dependency in the dfr framework
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Transforming patents into prior-art queries
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
PRES: a score metric for evaluating recall-oriented information retrieval applications
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Simple vs. sophisticated approaches for patent prior-art search
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Building queries for prior-art search
IRFC'11 Proceedings of the Second international conference on Multidisciplinary information retrieval facility
Patent query reduction using pseudo relevance feedback
Proceedings of the 20th ACM international conference on Information and knowledge management
Query performance prediction: evaluation contrasted with effectiveness
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Exploring patent passage retrieval using nouns phrases
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Leveraging conceptual lexicon: query disambiguation using proximity information for patent retrieval
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Patent prior art queries are full patent applications which are much longer than standard web search topics. Such queries are composed of hundreds of terms and do not represent a focused information need. One way to make the queries more focused is to select a group of key terms as representatives. Existing works show that such a selection to reduce patent queries is a challenging task mainly because of the presence of ambiguous terms. Given this setup, we present a query modeling approach where we utilize patent-specific characteristics to generate more precise queries. We propose to automatically disambiguate query terms by employing noun phrases that are extracted using the global analysis of the patent collection. We further introduce a method for predicting whether expansion using noun phrases would improve the retrieval effectiveness. Our experiments show that we can obtain almost 20% improvement by performing query expansion using the true importance of the noun phrase queries. Based on this observation, we introduce various features that can be used to estimate the importance of the noun phrase query. We evaluated the effectiveness of the proposed method on the patent prior art search collection CLEF-IP 2010. Our experimental results indicate that the proposed features make good predictors of the noun phrase importance, and selective application of noun phrase queries using the importance predictors outperforms existing query generation methods.