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
Optimal document-indexing vocabulary for MEDLINE
Information Processing and Management: an International Journal - Special issue: history of information science
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Retrieval evaluation with incomplete information
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Orthogonal negation in vector spaces for modelling word-meanings and document retrieval
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Improving the estimation of relevance models using large external corpora
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Voting for candidates: adapting data fusion techniques for an expert search task
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Using query contexts in information retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Selecting good expansion terms for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A simple and efficient sampling method for estimating AP and NDCG
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Regression Rank: Learning to Meet the Opportunity of Descriptive Queries
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Parallel boosted regression trees for web search ranking
Proceedings of the 20th international conference on World wide web
Bagging gradient-boosted trees for high precision, low variance ranking models
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Parameterized concept weighting in verbose queries
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Enhancing electronic medical record retrieval through semantic query expansion
Information Systems and e-Business Management
Exploiting term dependence while handling negation in medical search
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Combining multi-level evidence for medical record retrieval
Proceedings of the 2012 international workshop on Smart health and wellbeing
A task-specific query and document representation for medical records search
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Learning to combine representations for medical records search
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Inferring conceptual relationships to improve medical records search
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
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Negated language is frequently used by medical practitioners to indicate that a patient does not have a given medical condition. Traditionally, information retrieval systems do not distinguish between the positive and negative contexts of terms when indexing documents. For example, when searching for patients with angina, a retrieval system might wrongly consider a patient with a medical record stating ``no evidence of angina" to be relevant. While it is possible to enhance a retrieval system by taking into account the context of terms within the indexing representation of a document, some non-relevant medical records can still be ranked highly, if they include some of the query terms with the intended context. In this paper, we propose a novel learning framework that effectively handles negated language. Based on features related to the positive and negative contexts of a term, the framework learns how to appropriately weight the occurrences of the opposite context of any query term, thus preventing documents that may not be relevant from being retrieved. We thoroughly evaluate our proposed framework using the TREC 2011 and 2012 Medical Records track test collections. Our results show significant improvements over existing strong baselines. In addition, in combination with a traditional query expansion and a conceptual representation approach, our proposed framework could achieve a retrieval effectiveness comparable to the performance of the best TREC 2011 and 2012 systems, while not addressing other challenges in medical records search, such as the exploitation of semantic relationships between medical terms.