New wine in old wineskins: a morphology-based approach to translate medical terminology
Authors: Raffaele Guarasci, Alessandro Maisto (Università di Salerno, 2015)
Topic: In-domain IR and IE

Abstract
In this work we introduce the first steps toward the development of a machine translation system for medical terminology. We explore the possibility of basing a machine translation task in the medical domain on morphology. Starting from neoclassical formative elements, or confixes, we started building MedIta, a cross-language ontology of medical morphemes, aiming to offer a standardized medical consistent resource that includes distributional and semantic information of medical morphemes. Using this information, we have built an ontology-driven Italian-English machine translation prototype, based on a set of Finite State Transducers, and we have carried out an experiment on Orphanet medical corpus to evaluate the feasibility of this approach.

Introduction
Automating Machine Translation (MT) of a technical language is a challenging task that requires an in-depth analysis both from a linguistic point of view and as regards the implementation of a complex system. This becomes even more complex in medical language. Indeed the translation of medical terminology must always be validated by a domain expert following official classification standards. For this reason currently there are no translation support tools specifically created for the medical domain. In this work we propose an MT system based on a set of Finite State Transducers that uses cross-language morpheme information provided by a lexical resource. The underlying idea is that in a technical language a morpho-semantic approach AUTHOR may be more effective than a probabilistic one in term-by-term translation tasks. Even though our approach could seem a bit ``old fashioned'', we must consider that proper nature of medical language, fully based by morphemes derived from neoclassical formative elements AUTHOR. Neoclassical formative elements are morphological elements that come into being from Latin and Greek words, they combine with each other following compositional morphology rules. Due to the heterogeneous nature of these elements, they have received different definitions, we prefer to use the term confixes, a morpheme with full semantic value, which has been predominantly used in the literature AUTHOR. In this work we focused only on word formation related to the medical domain.

Conclusion
In this work we presented a morphology-based machine translation prototype specifically suited for medical terminology. The prototype uses ontologies of morphemes and Finite State Transducers. Even though the approach may seem a little out-of-date, the preliminary results showed that it can work as well as a probabilistic system in such a specific domain. It is worth mentioning that at this early stage we tested the prototype only on samples, since the evaluation is an extremely time-consuming task: every translated term must be manually compared with one or more medical standards. Medical standards are often not aligned, therefore an Orpha-number (disease id) does not necessarily match a disease listed in ICD-10. Moreover, these resources are not easily usable in an automated way, therefore the evaluation should entirely be done manually.