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
A Markov random field model for term dependencies
Proceedings of the 28th 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
Incorporating term dependency in the dfr framework
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Aggregating evidence from hospital departments to improve medical records search
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
A task-specific query and document representation for medical records search
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
An adaptive evidence weighting method for medical record 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
Learning to handle negated language in medical records search
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Learning to selectively rank patients' medical history
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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In medical records, negative qualifiers, e.g. no or without, are commonly used by health practitioners to identify the absence of a medical condition. Without considering whether the term occurs in a negative or positive context, the sole presence of a query term in a medical record is insufficient to imply that the record is relevant to the query. In this paper, we show how to effectively handle such negation within a medical records information retrieval system. In particular, we propose a term representation that tackles negated language in medical records, which is further extended by considering the dependence of negated query terms. We evaluate our negation handling technique within the search task provided by the TREC Medical Records 2011 track. Our results, which show a significant improvement upon a system that does not consider negated context within records, attest the importance of handling negation.