A Framework to Generate Sets of Terms from Large Scale Medical Vocabularies for Natural Language Processing

Abstract : In this paper we present our ongoing work on integrating large-scale terminological information into NLP tools. We focus on the problem of selecting and generating a set of suitable terms from the resources, based on deletion, modification and addition rules. We propose a general framework in which the raw data of the resources are first loaded into a knowledge base (KB). The selection and generation rules are then defined in a declarative way using query templates in the query language of the KB system. We illustrate the use of this framework to select and generate term sets from a UMLS dataset.
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Communication dans un congrès
10th International Conference on Computational Semantics, Mar 2013, Potsdam, Germany
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Salah Ait-Mokhtar, Caroline Hagege, Pajolma Rupi. A Framework to Generate Sets of Terms from Large Scale Medical Vocabularies for Natural Language Processing. 10th International Conference on Computational Semantics, Mar 2013, Potsdam, Germany. 〈hal-01172419〉

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