TermWise: Leveraging Big Data for Terminological Support in Legal Translation

Kris Heylen (1) , Stephen Bond (2) , Dirk de Hertog (2) , Hendrik Kockaert (3) , Frieda Steurs (3) , Ivan Vulić (4)

1 QLVL - QLVL, Linguistics Department (KU Leuven)
2 QLVL
3 QLVL
4 LIIR

Increasingly, large bilingual document collections are being made available online, especially in the legal domain. This type of Big Data is a valuable resource that specialized translators exploit to search for informative examples of how domain-specific expressions should be translated. However, general purpose search engines are not optimized to retrieve previous translations that are maximally relevant to a translator. In this paper, we report on the TermWise project, a cooperation of terminologists, corpus linguists and computer scientists, that aims to leverage big online translation data for terminological support to legal translators at the Belgian Federal Ministry of Justice. The project developed dedicated knowledge extraction algorithms and a server-based tool to provide translators with the most relevant previous translations of domain-specific expressions relative to the current translation assignment. In the paper, we give an overview of the system, give a demo of the user interface and then discuss, more in general, the possibilities of mining big data to support specialized translation.

Domaines: Informatique et langage [cs.CL]

Kris Heylen, Stephen Bond, Dirk de Hertog, Hendrik Kockaert, Frieda Steurs, et al.. TermWise: Leveraging Big Data for Terminological Support in Legal Translation. Terminology and Knowledge Engineering 2014, Jun 2014, Berlin, Germany. 10 p. ⟨hal-01005842⟩