Foundations of statistical natural language processing
Foundations of statistical natural language processing
Parsimonious language models for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Proposal of two-stage patent retrieval method considering the claim structure
ACM Transactions on Asian Language Information Processing (TALIP)
Introduction to the special issue on patent processing
Information Processing and Management: an International Journal
Enhancing patent retrieval by citation analysis
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Retrievability: an evaluation measure for higher order information access tasks
Proceedings of the 17th ACM conference on Information and knowledge management
Toward a more rational patent search paradigm
Proceedings of the 1st ACM workshop on Patent information retrieval
Transforming patents into prior-art queries
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A query model based on normalized log-likelihood
Proceedings of the 18th ACM conference on Information and knowledge management
Automatic query generation for patent search
Proceedings of the 18th ACM conference on Information and knowledge management
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
Search system requirements of patent analysts
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
CLEF'10 Proceedings of the 2010 international conference on Multilingual and multimodal information access evaluation: cross-language evaluation forum
Improving retrievability of patents in prior-art search
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Knowledge modeling in prior art search
IRFC'10 Proceedings of the First international Information Retrieval Facility conference on Adbances in Multidisciplinary Retrieval
Effective query generation and postprocessing strategies for prior art patent search
Journal of the American Society for Information Science and Technology
Automatic refinement of patent queries using concept importance predictors
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Learning-Based pseudo-relevance feedback for patent retrieval
IRFC'12 Proceedings of the 5th conference on Multidisciplinary Information Retrieval
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Prior-art search is a critical step in the examination procedure of a patent application. This study explores automatic query generation from patent documents to facilitate the time-consuming and labor-intensive search for relevant patents. It is essential for this task to identify discriminative terms in different fields of a query patent, which enables us to distinguish relevant patents from non-relevant patents. To this end we investigate the distribution of terms occurring in different fields of the query patent and compare the distributions with the rest of the collection using language modeling estimation techniques. We experiment with term weighting based on the Kullback-Leibler divergence between the query patent and the collection and also with parsimonious language model estimation. Both of these techniques promote words that are common in the query patent and are rare in the collection. We also incorporate the classification assigned to patent documents into our model, to exploit available human judgements in the form of a hierarchical classification. Experimental results show that the retrieval using the generated queries is effective, particularly in terms of recall, while patent description is shown to be the most useful source for extracting query terms.