LIST-LUX: Disorder identification from clinical texts

Auteurs

A. Ben Abacha, A. Karanasiou, Y. Mrabet, and J. C. Dos Reis

Référence

in Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, June 4-5, 2015, pp. 427-432, 2015

Description

This paper describes our participation in task 14 of SemEval 2015. This task focuses on the analysis of clinical texts and includes: (i) the recognition of the span of a disorder mention and (ii) its normalization to a unique concept identifier in the UMLS/SNOMED-CT terminology. We propose a two-step approach which relies first on Conditional Random Fields to detect textual mentions of disorders using different lexical, syntactic, orthographic and semantic features such as ontologies and, second, on a similarity measure and SNOMED to determine the relevant CUI. We present and discuss the obtained results on the development corpus and the official test corpus.

Lien

http://alt.qcri.org/semeval2015/cdrom/pdf/SemEval074.pdf