Automatic acquisition of bilingual rules for extraction of bilingual word pairs from parallel corpora

Echizen-ya, Hiroshi; Araki, Kenji; Momouchi, Yoshio

doi:10.3115/1631850.1631860

Cited by 1 publication

(2 citation statements)

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“…However, the approach requires large comparable corpora, the collection of which presents non-trivial challenges. Others have made similar mutual-translation assumptions for lexical acquisition (Echizen-ya, et al, 2005;Kaji & Aizono, 1996;Rapp, 1999;Tanaka & Iwasaki, 1996). Most make use of either parallel corpora or a bilingual dictionary for the task of bilingual term extraction.…”

Section: Related Workmentioning

confidence: 99%

“…Most make use of either parallel corpora or a bilingual dictionary for the task of bilingual term extraction. Echizen-ya, et al (2005) avoided using a bilingual dictionary, but required a parallel corpus to achieve their goal; whereas Fung (2000) and others have relied on pre-existing bilingual dictionaries. In either case, large bilingual resources of some kind are required.…”

Section: Related Workmentioning

confidence: 99%

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Leveraging reusability

Murray

Dorr

Lin

et al. 2006

Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the ACL - ACL '06

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Thesauri and ontologies provide important value in facilitating access to digital archives by representing underlying principles of organization. Translation of such resources into multiple languages is an important component for providing multilingual access. However, the specificity of vocabulary terms in most ontologies precludes fully-automated machine translation using general-domain lexical resources. In this paper, we present an efficient process for leveraging human translations when constructing domain-specific lexical resources. We evaluate the effectiveness of this process by producing a probabilistic phrase dictionary and translating a thesaurus of 56,000 concepts used to catalogue a large archive of oral histories. Our experiments demonstrate a cost-effective technique for accurate machine translation of large ontologies.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%