Abstract
Medical texts are filled with mentions of diseases, disorders, and other clinical conditions, with many different surface forms relating to the same condition. We describe MINERAL, a system for extraction and normalization of disease mentions in clinical text, with which we participated in the Task 14 of SemEval 2015 evaluation campaign. MINERAL relies on a conditional random fields-based model with a rich set of features for mention detection, and a semantic textual similarity measure for entity linking. MINERAL reaches joint extraction and linking performance of 75.9% relaxed F1score (strict score of 72.7%) and ranks fourth among 16 participating teams.
Cite
CITATION STYLE
Glavaš, G. (2015). TAKELAB: Medical Information Extraction and Linking with MINERAL. 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. 389–393). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s15-2067
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