Paper: BioinformaticsUA: Concept Recognition in Clinical Narratives Using a Modular and Highly Efficient Text Processing Framework

ACL ID S14-2019
Title BioinformaticsUA: Concept Recognition in Clinical Narratives Using a Modular and Highly Efficient Text Processing Framework
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

Clinical texts, such as discharge sum- maries or test reports, contain a valuable amount of information that, if efficiently and effectively mined, could be used to infer new knowledge, possibly leading to better diagnosis and therapeutics. With this in mind, the SemEval-2014 Analysis of Clinical Text task aimed at assessing and improving current methods for identi- fication and normalization of concepts oc- curring in clinical narrative. This paper describes our approach in this task, which was based on a fully modular architec- ture for text mining. We followed a pure dictionary-based approach, after perform- ing error analysis to refine our dictionaries. We obtained an F-measure of 69.4% in the entity recognition task, achieving the second best precision over all submitted runs (81.3%),...