Natural language vs. Boolean query evaluation: a comparison of retrieval performance
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Proposal of two-stage patent retrieval method considering the claim structure
ACM Transactions on Asian Language Information Processing (TALIP)
Enhancing patent retrieval by citation analysis
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Transforming patents into prior-art queries
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Improving retrievability of patents with cluster-based pseudo-relevance feedback documents selection
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
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We present a reranking-based patent retrieval system where the query text is a patent claim, which may be from an existing patent. The novelty of our approach is the automatic generating of training data for learning the ranker. The ranking is based on several features of the candidate patent, such as the text similarity to the claim, international patent code overlap, and internal citation structure of the candidates. Our approach more than doubles the average number of relevant patents in the top 5 over a strong baseline retrieval system.