Abstract
This paper describes the approach used by ezDI at the SemEval 2015 Task-14:”Analysis of Clinical Text”. The task was divided into two embedded tasks. Task-1 required determining disorder boundaries (including the discontiguous ones) from a given set of clinical notes and normalizing the disorders by assigning a unique CUI from the UMLS/SNOMEDCT. Task-2 was about finding different type of modifiers for given disorder mention. Task-2 was divided further into two subtasks. In subtask-2a, gold set of disorder was already provided and system needed to just fill modifier types into the pre-specified slots. Subtask 2b did not provide any gold set of disorders and both the disorders and its related modifiers are to be identified by the system itself. In Task-1 our system was ranked first with F-score of 0.757 for strict evaluation and 0.788 for relaxed evaluation. In both Task-2a and 2b our system was placed second with weighted F-score of 0.88 and 0.795 respectively.
Cite
CITATION STYLE
Pathak, P., Patel, P., Panchal, V., Soni, S., Dani, K., Choudhary, N., & Patel, A. (2015). ezDI: A Supervised NLP System for Clinical Narrative Analysis. In SemEval 2015 - 9th International Workshop on Semantic Evaluation, co-located with the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2015 - Proceedings (pp. 412–416). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s15-2071
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