Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Query-based sampling of text databases
ACM Transactions on Information Systems (TOIS)
Simple BM25 extension to multiple weighted fields
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Term distillation in patent retrieval
PATENT '03 Proceedings of the ACL-2003 workshop on Patent corpus processing - Volume 20
Introduction to the special issue on patent processing
Information Processing and Management: an International Journal
Information Processing and Management: an International Journal
Retrievability: an evaluation measure for higher order information access tasks
Proceedings of the 17th ACM conference on Information and knowledge management
Accessibility in information retrieval
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Applications of web query mining
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
Improving retrievability and recall by automatic corpus partitioning
Transactions on large-scale data- and knowledge-centered systems II
Improving retrievability and recall by automatic corpus partitioning
Transactions on large-scale data- and knowledge-centered systems II
Applying key phrase extraction to aid invalidity search
Proceedings of the 13th International Conference on Artificial Intelligence and Law
Improving retrievability of patents in prior-art search
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
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Most information retrieval settings, such as web search, are typically precision-oriented, i.e. they focus on retrieving a small number of highly relevant documents. However, in specific domains, such as patent retrieval or law, recall becomes more relevant than precision: in these cases the goal is to find all relevant documents, requiring algorithms to be tuned more towards recall at the cost of precision. This raises important questions with respect to retrievability and search engine bias: depending on how the similarity between a query and documents is measured, certain documents may be more or less retrievable in certain systems, up to some documents not being retrievable at all within common threshold settings. Biases may be oriented towards popularity of documents (increasing weight of references), towards length of documents, favour the use of rare or common words; rely on structural information such as metadata or headings, etc. Existing accessibility measurement techniques are limited as they measure retrievability with respect to all possible queries. In this paper, we improve accessibility measurement by considering sets of relevant and irrelevant queries for each document. This simulates how recall oriented users create their queries when searching for relevant information. We evaluate retrievability scores using a corpus of patents from US Patent and Trademark Office.