Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Advances in Informational Retrieval: Recent Research from the Center for Intelligent Information Retrieval
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Term distillation in patent retrieval
PATENT '03 Proceedings of the ACL-2003 workshop on Patent corpus processing - Volume 20
Using controlled query generation to evaluate blind relevance feedback algorithms
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
Introduction to the special issue on patent processing
Information Processing and Management: an International Journal
A cluster-based resampling method for pseudo-relevance feedback
Proceedings of the 31st 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
Comparing metrics across TREC and NTCIR: the robustness to system bias
Proceedings of the 17th ACM conference on Information and knowledge management
On the relationship between effectiveness and accessibility
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Unsupervised learning for reranking-based patent retrieval
PaIR '10 Proceedings of the 3rd international workshop on Patent information retrieval
Improving access to large patent corpora
Transactions on large-scale data- and knowledge-centered systems II
Improving access to large patent corpora
Transactions on large-scale data- and knowledge-centered systems II
Expanding queries with term and phrase translations in patent retrieval
IRFC'11 Proceedings of the Second international conference on Multidisciplinary information retrieval facility
Applying key phrase extraction to aid invalidity search
Proceedings of the 13th International Conference on Artificial Intelligence and Law
Improving document clustering using Okapi BM25 feature weighting
Information Retrieval
Improving retrievability of patents in prior-art search
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Relating retrievability, performance and length
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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High findability of documents within a certain cut-off rank is considered an important factor in recall-oriented application domains such as patent or legal document retrieval. Findability is hindered by two aspects, namely the inherent bias favoring some types of documents over others introduced by the retrieval model, and the failure to correctly capture and interpret the context of conventionally rather short queries. In this paper, we analyze the bias impact of different retrieval models and query expansion strategies. We furthermore propose a novel query expansion strategy based on document clustering to identify dominant relevant documents. This helps to overcome limitations of conventional query expansion strategies that suffer strongly from the noise introduced by imperfect initial query results for pseudo-relevance feedback documents selection. Experiments with different collections of patent documents suggest that clustering based document selection for pseudo-relevance feedback is an effective approach for increasing the findability of individual documents and decreasing the bias of a retrieval system.