Machine Learning Based Finding of Similar Sentences from French Clinical Notes

Author:

Dramé Khadim,Diallo Gayo,Sambe Gorgoumack

Publisher

Springer International Publishing

Reference43 articles.

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2. Agirre, E., et al.: SemEval-2016 task 1: semantic textual similarity, monolingual and cross-lingual evaluation. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 497–511. Association for Computational Linguistics, San Diego (2016). https://doi.org/10.18653/v1/S16-1081, https://www.aclweb.org/anthology/S16-1081

3. Bird, S., Loper, E.: NLTK: the natural language toolkit. In: Proceedings of the ACL Interactive Poster and Demonstration Sessions, pp. 214–217. Association for Computational Linguistics, Barcelona (2004). https://www.aclweb.org/anthology/P04-3031

4. Bodenreider, O.: The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32(Database issue), D267–D270 (2004). https://doi.org/10.1093/nar/gkh061, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC308795/

5. Cardon, R., Grabar, N., Grouin, C., Hamon, T.: Presentation of the DEFT 2020 Challenge: open domain textual similarity and precise information extraction from clinical cases. In: Actes de la 6e conférence conjointe Journées d’Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Atelier DÉfi Fouille de Textes, pp. 1–13. ATALA et AFCP, Nancy (2020). https://www.aclweb.org/anthology/2020.jeptalnrecital-deft.1

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