Generating and using probabilistic morphological resources for the biomedical domain

• Main Authors: Claveau, Vincent, Kijak, Ewa
• Other Authors: Multimedia content-based indexing (TEXMEX), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)
• Format: Other/Unknown Material
• Language: English
• Published: HAL CCSD 2014
• Subjects: Morpho-semantic analysis; biomedical terminology; probabilistic morphological resources; lang; anthro-se; Pivot; Iceland
• Online Access: https://hal.archives-ouvertes.fr/hal-01027778/file/Claveau_Kijak_LREC14.pdf; https://hal.archives-ouvertes.fr/hal-01027778

International audience In most Indo-European languages, many biomedical terms are rich morphological structures composed of several constituents mainly originating from Greek or Latin. The interpretation of these compounds are keystones to access information. In this paper, we present morphological resources aiming at coping with these biomedical morphological compounds. Following previous work (Claveau and Kijak, 2011; Claveau, 2012), these resources are automatically built using Japanese terms in Kanjis as a pivot language and alignment techniques. We show how these alignment information can be used for segmenting compounds, attaching semantic interpretation to each part, proposing definitions (gloses) of the compounds. When possible, these tasks are compared with state-of-the-art tools, and the results show the interest of our automatically built probabilistic resources.