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Two approaches to compilation of bilingual multi-word terminology lists from lexical resources

Published online by Cambridge University Press:  28 January 2020

Branislava Šandrih Open the ORCID record for Branislava Šandrih [Opens in a new window] ,
Cvetana Krstev  and
Ranka Stanković
Branislava Šandrih*
Affiliation:
Faculty of Philology, University of Belgrade, Belgrade, Serbia
Cvetana Krstev
Affiliation:
Faculty of Philology, University of Belgrade, Belgrade, Serbia
Ranka Stanković
Affiliation:
Faculty of Mining and Geology, University of Belgrade, Belgrade, Serbia
*
*Corresponding author. Email: [email protected]
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Abstract

In this paper, we present two approaches and the implemented system for bilingual terminology extraction that rely on an aligned bilingual domain corpus, a terminology extractor for a target language, and a tool for chunk alignment. The two approaches differ in the way terminology for the source language is obtained: the first relies on an existing domain terminology lexicon, while the second one uses a term extraction tool. For both approaches, four experiments were performed with two parameters being varied. In the experiments presented in this paper, the source language was English, and the target language Serbian, and a selected domain was Library and Information Science, for which an aligned corpus exists, as well as a bilingual terminological dictionary. For term extraction, we used the FlexiTerm tool for the source language and a shallow parser for the target language, while for word alignment we used GIZA++. The evaluation results show that for the first approach the F1 score varies from 29.43% to 51.15%, while for the second it varies from 61.03% to 71.03%. On the basis of the evaluation results, we developed a binary classifier that decides whether a candidate pair, composed of aligned source and target terms, is valid. We trained and evaluated different classifiers on a list of manually labeled candidate pairs obtained after the implementation of our extraction system. The best results in a fivefold cross-validation setting were achieved with the Radial Basis Function Support Vector Machine classifier, giving a F1 score of 82.09% and accuracy of 78.49%.

Keywords

Language resourcesMachine translationTerminology extractionText classification
Type
Article
Information
Natural Language Engineering , Volume 26 , Issue 4 , July 2020 , pp. 455 - 479
Copyright
© Cambridge University Press 2020

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Footnotes

†

This research was supported by Serbian Ministry of Education and Science under the grants #III 47003 and 178006.

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