Abstract
We present work on tuning the Heideltime system for identifying time expressions in clinical texts in English and French languages. The main amount of the method is related to the enrichment and adaptation of linguistic resources to identify Timex3 clinical expressions and to normalize them. The test of the adapted versions have been done on the i2b2/VA 2012 corpus for English and a collection of clinical texts for French, which have been annotated for the purpose of this study. We achieve a 0.8500 F-measure on the recognition and normalization of temporal expressions in English, and up to 0.9431 in French. Future work will allow to improve and consolidate the results.
Cite
CITATION STYLE
Hamon, T., & Grabar, N. (2014). Tuning HeidelTime for identifying time expressions in clinical texts in English and French. In Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis, Louhi 2014 at the 14th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2014 (pp. 101–105). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/w14-1116
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