Tuning HeidelTime for identifying time ...
Document type :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Title :
Tuning HeidelTime for identifying time expressions in clinical texts in English and French
Author(s) :
Hamon, Thierry [Auteur]
Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur [LIMSI]
Université Paris 13 [UP13]
Grabar, Natalia [Auteur]
Savoirs, Textes, Langage (STL) - UMR 8163 [STL]
Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur [LIMSI]
Université Paris 13 [UP13]
Grabar, Natalia [Auteur]
![refId](/themes/Mirage2//images/idref.png)
Savoirs, Textes, Langage (STL) - UMR 8163 [STL]
Conference title :
International Workshop on Health Text Mining and Information Analysis
City :
Gothenburg
Country :
Suède
Start date of the conference :
2014-01-01
English keyword(s) :
Clinical texts
time expression
Natural Language Processing
time expression
Natural Language Processing
HAL domain(s) :
Informatique [cs]
Informatique [cs]/Informatique et langage [cs.CL]
Informatique [cs]/Informatique et langage [cs.CL]
English abstract : [en]
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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
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