Class-based n-gram models of natural language
Computational Linguistics
Linguistically motivated large-scale NLP with C&C and boxer
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Clinical entity recognition using structural support vector machines with rich features
Proceedings of the ACM sixth international workshop on Data and text mining in biomedical informatics
DTMBIO 2013: international workshop on data and text mining in biomedical informatics
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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The growth of medical and clinical textual datasets has fostered research interests in methods for storing, retrieving and extracting of pertinent data. In more recent years, shared tasks and more comprehensive data sharing agreements have seen a further growth in the research area spanning Natural Language Processing (NLP) and Information Retrieval (IR) to aid the world of healthcare. Frequently NLP applications such as Medical Entity Recognition (MER), are motivated within the context of improving IR system performance. In this paper, we investigate the application of MER to a clinical retrieval system in the context of shared tasks in the respective areas. Namely, we aim to add structure to previously unstructured clinical reports and query sets. We evaluate the performance of MER on the query set, highlighting issues in constructing queries in a clinical setting. Further to this, we evaluate the performance of structuring queries on a retrieval dataset. We find that while structuring queries improves performance on complex queries that contain many term dependencies, there is a larger issue of linguistic variation found in clinical texts that must also be addressed.