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Polibits
On-line version ISSN 1870-9044
Polibits n.43 México Jan./Jun. 2011
Low Cost Construction of a Multilingual Lexicon from Bilingual Lists
Lian Tze Lim*, Bali RanaivoMalançon**, and Enya Kong Tang***
Natural Language Processing Special Interest Group, Faculty of Information Technology, Multimedia University, Malaysia (email: *liantze@gmail.com, **ranaivo@mmu.edu.my, ***enyakong@mmu.edu.my).
Manuscript received November 2, 2010.
Manuscript accepted for publication January 22, 2011.
Abstract
Manually constructing multilingual translation lexicons can be very costly, both in terms of time and human effort. Although there have been many efforts at (semi)automatically merging bilingual machine readable dictionaries to produce a multilingual lexicon, most of these approaches place quite specific requirements on the input bilingual resources. Unfortunately, not all bilingual dictionaries fulfil these criteria, especially in the case of underresourced language pairs. We describe a low cost method for constructing a multilingual lexicon using only simple lists of bilingual translation mappings. The method is especially suitable for underresourced language pairs, as such bilingual resources are often freely available and easily obtainable from the Internet, or digitised from simple, conventional paperbased dictionaries. The precision of random samples of the resultant multilingual lexicon is around 0.700.82, while coverage for each language, precision and recall can be controlled by varying threshold values. Given the very simple input resources, our results are encouraging, especially in incorporating underresourced languages into multilingual lexical resources.
Key words: Lexical resources, multilingual lexicon, underresourced languages.
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ACKNOWLEDGMENT
The work reported in this paper is supported by a Fundamental Research Grant (FRGS/1/10/TK/MMU/02/02) from the Malaysian Ministry of Higher Education. We thank the evaluators who participated in the results evaluation, and the two anonymous reviewers for their comments on improving this paper.
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