Word sense disambiguation using a second language monolingual corpus
Computational Linguistics
Generalized L.R. Parsing
A pattern-based machine translation system extended by example-based processing
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Word-sense disambiguation using statistical methods
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Pattern-based machine translation
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Identification of Subject Shareness for Korean-English Machine Translation
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
| Hi-index | 0.00 |
Pattern-Based Machine Translation is one of the machine translation methods which performs syntactic analysis and structure transfer at the same time using bilingual patterns. PBMT is used to expand the length of patterns up to sentence-length in order to reduce ambiguities in translation, but it brought out the problem of rapidly increased patterns. We propose a model which shortens the length of patterns to phrase-length and reduces ambiguities in translation by using two level translation pattern selection method. In the first level, the proper translation patterns are selected by using a hybrid method of exact example matching and semantic constraint by thesaurus. In the second level, the most natural translation pattern for the verb phrase is selected among the selected translation pattern categories by using statistic information of the target language. By using this proposed model, we could shorten the length of patterns without raising the ambiguities in translation.