Problems of machine translation systems: Effect of cultural differences on sentence structure

Nitta, Yoshihiko

doi:10.1016/0167-739x(86)90004-x

Cited by 8 publications

(1 citation statement)

References 3 publications

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“…With careful observation of structural transfer ambiguity, we found that the major factor was a kind of stylistic difference which resulted from a different way of thinking and wording [15]. Thus, the tendency of restricting a Korean subject only to animate entities and of allowing an English subject to be other inanimate entities, such as instrument, natural force and cause/reason is studied in [16].…”

Section: Structural Transfer Ambiguity Resolutionmentioning

confidence: 99%

Resolving Structural Transfer Ambiguity in Chinese-to-Korean Machine Translation

Kim¹,

Zheng²,

Li³

et al. 2003

Int. J. Comp. Proc. Lang.

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We propose a new prototype Chinese-to-Korean machine translation system called TOTAL-CK, a prototype transfer based MT system designed for the large-scale practical domain. TOTAL-CK consists of the components of analysis, transfer, and generation. In this paper, we mainly discuss the transfer issues resulting from stylistic structural differences between Chinese and Korean. The dependency grammar formalism is employed for Chinese parsed trees and their equivalent Korean parsed trees. We deal with structural transfer ambiguities that arise when a given source language syntactic pattern is potentially translated by a number of different target language syntactic patterns. Also, structural transfer ambiguities are the major complex transfer problem appearing during dependency transferring, in an SVO pattern that most frequently occurs in Chinese sentences. There are three types of structural transfer ambiguity: transitive transfer, subject-governing transfer and verbgoverning transfer. They are discovered by analyzing the ambiguity and the related formalism. Finally, we present a thesaurus-based disambiguation approach for dependency structural transfer, which is adopted in TOTAL-CK. Our approach achieves 94.6% in precision for SVO sentences and 93.5% for overall sentences.

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Section: Structural Transfer Ambiguity Resolutionmentioning

confidence: 99%