A framework of a mechanical translation between Japanese and English by analogy principle
Proc. of the international NATO symposium on Artificial and human intelligence
Introduction to Algorithms
Automated generalization of translation examples
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Example-based machine translation using DP-matching between word sequences
DMMT '01 Proceedings of the workshop on Data-driven methods in machine translation - Volume 14
A corpus-centered approach to spoken language translation
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 2
Acquiring synonyms from monolingual comparable texts
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
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Example-based machine translation (EBMT) is based on a bilingual corpus. In EBMT, sentences similar to an input sentence are retrieved from a bilingual corpus and then output is generated from translations of similar sentences. Therefore, a similarity measure between the input sentence and each sentence in the bilingual corpus is important for EBMT. If some similar sentences are missed from retrieval, the quality of translations drops. In this paper, we describe a method to acquire synonymous expressions from a bilingual corpus and utilize them to expand retrieval of similar sentences. Synonymous expressions are acquired from differences in synonymous sentences. Synonymous sentences are clustered by the equivalence of translations. Our method has the advantage of not relying on rich linguistic knowledge, such as sentence structure and dictionaries. We demonstrate the effect on applying our method to a simple EBMT.