Proceedings of the 16th Conference on Computational Linguistics - 1996
DOI: 10.3115/992628.992653
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Classifiers in Japanese-to-English machine translation

Abstract: This l)a.i)cr t)rot)oses ;m mmlysis of classifters into ['our ma,jor l;yl)

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Cited by 18 publications
(27 citation statements)
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“…Annotators are asked to select which of two randomly-ordered translations they prefer, one from each system (Bond, Ogura, and Ikehara, 1995;Schwartz, Aikawa, and Quirk, 2003), often over a reference set of translation pairs (Ikehara, Shirai, and Ogura, 1994).…”
Section: Past and Current Methodologiesmentioning
confidence: 99%
“…Annotators are asked to select which of two randomly-ordered translations they prefer, one from each system (Bond, Ogura, and Ikehara, 1995;Schwartz, Aikawa, and Quirk, 2003), often over a reference set of translation pairs (Ikehara, Shirai, and Ogura, 1994).…”
Section: Past and Current Methodologiesmentioning
confidence: 99%
“…In the Thai language, as well as in some other Asian languages such as Japanese and Chinese , classifiers find significant use in quantitative noun phrases (Sornlertlamvanich et al, 1994 ;Bond et al, 1996). From our study, we realized that classifiers do not only help in expressing quantitative noun phrases, but also play a very important role in forming many types of phrases, including relative pronoun phrases, noun phrases and adverb phrases (see (Sornlertlamvanich et al, 1994) for a detailed discussion).…”
Section: Structure Of the Text Corpusmentioning
confidence: 79%
“…However, our framework was defined and tested on the restrictive domain of appointment scheduling. Most of the really difficult cases for article selection, as for example generics, do not occur in this domain, whilst both (Murata and Nagao, 1993) and (Bond et al, 1995) build their theories around the problem of identifying these. There are no statistics on the performance of their systems on a corpus that does not contain any generics.…”
Section: Comparison To Previous Approachesmentioning
confidence: 99%
“…This approach assigns the correct value in 85,5% of the cases when used with the training data, and 68,9% with unseen data. (Bond et al, 1995) show how the percentage of noun phrases generated with correct use of articles and number in a Japanese to English machine translation system can be increased by applying heuristic rules to distinguish between 'generic', 'referential' and 'ascriptive' uses of noun phrases. These rules are ordered in a hierarchical manner, with later rules over-ruling earlier ones.…”
Section: Introductionmentioning
confidence: 99%