MHeTRep: A multilingual semantically tagged health terms repository

Abstract

Abstract This paper presents MHeTRep, a multilingual medical terminology and the methodology followed for its compilation. The multilingual terminology is organised into one vocabulary for each language. All the terms in the collection are semantically tagged with a tagset corresponding to the top categories of Snomed-CT ontology. When possible, the individual terms are linked to their equivalent in the other languages. Even though many NLP resources and tools claim to be domain independent, their application to specific tasks can be restricted to specific domains, otherwise their performance degrades notably. As the accuracy of NLP resources drops heavily when applied in environments different from which they were built, a tuning to the new environment is needed. Usually, having a domain terminology facilitates and accelerates the adaptation of general domain NLP applications to a new domain. This is particularly important in medicine, a domain living moments of great expansion. The proposed method takes Snomed-CT as starting point. From this point and using 13 multilingual resources, covering the most relevant medical concepts such as drugs, anatomy, clinical findings and procedures, we built a large resource covering seven languages totalling more than two million semantically tagged terms. The resulting collection has been intensively evaluated in several ways for the involved languages and domain categories. Our hypothesis is that MHeTRep can be used advantageously over the original resources for a number of NLP use cases and likely extended to other languages.