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A classification approach for detecting cross-lingual biomedical term translations

Published online by Cambridge University Press:  14 December 2015

H. HAKAMI  and
D. BOLLEGALA
H. HAKAMI
Affiliation:
Computer Science Department, Taif University, Saudi Arabia e-mail: hoda.h@tu.edu.sa
D. BOLLEGALA
Affiliation:
Department of Computer Science, The University of Liverpool, UK e-mail: danushka.bollegala@liverpool.ac.uk
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Abstract

Finding translations for technical terms is an important problem in machine translation. In particular, in highly specialized domains such as biology or medicine, it is difficult to find bilingual experts to annotate sufficient cross-lingual texts in order to train machine translation systems. Moreover, new terms are constantly being generated in the biomedical community, which makes it difficult to keep the translation dictionaries up to date for all language pairs of interest. Given a biomedical term in one language (source language), we propose a method for detecting its translations in a different language (target language). Specifically, we train a binary classifier to determine whether two biomedical terms written in two languages are translations. Training such a classifier is often complicated due to the lack of common features between the source and target languages. We propose several feature space concatenation methods to successfully overcome this problem. Moreover, we study the effectiveness of contextual and character n-gram features for detecting term translations. Experiments conducted using a standard dataset for biomedical term translation show that the proposed method outperforms several competitive baseline methods in terms of mean average precision and top-k translation accuracy.

Type
Articles
Information
Natural Language Engineering , Volume 23 , Issue 1 , January 2017 , pp. 31 - 51
Copyright
Copyright © Cambridge University Press 2015 

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