본문 바로가기
  • Home

Target Word Selection for English-Korean Machine Translation System using Multiple Knowledge

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2006, 11(5), pp.75-86
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

이기영 1 KIM HAN WOO 1

1한양대학교

Candidate

ABSTRACT

Target word selection is one of the most important and difficult tasks in English-Korean Machine Translation. It effects on the translation accuracy of machine translation systems. In this paper, we present a new approach to select Korean target word for an English noun with translation ambiguities using multiple knowledge such as verb frame patterns, sense vectors based on collocations, statistical Korean local context information and co-occurring POS information. Verb frame patterns constructed with dictionary and corpus play an important role in resolving the sparseness problem of collocation data. Sense vectors are a set of collocation data when an English word having target selection ambiguities is to be translated to specific Korean target word. Statistical Korean local context Information is an N-gram information generated using Korean corpus. The co-occurring POS information is a statistically significant POS clue which appears with ambiguous word. The experiment showed promising results for diverse sentences from web documents.

Citation status

* References for papers published after 2022 are currently being built.