Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
Language Grid: An Infrastructure for Intercultural Collaboration
SAINT '06 Proceedings of the International Symposium on Applications on Internet
Effects of machine translation on collaborative work
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
Word sense disambiguation vs. statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Improving statistical machine translation using lexicalized rule selection
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Maximum entropy based rule selection model for syntax-based statistical machine translation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Context-based approach for pivot translation services
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Effective use of linguistic and contextual information for statistical machine translation
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
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Consistent word selection in machine translation is currently realized by resolving word sense ambiguity through the context of a single sentence or neighboring sentences. However, consistent word selection over the whole article has yet to be achieved. Consistency over the whole article is extremely important when applying machine translation to collectively developed documents like Wikipedia. In this paper, we propose to consider constraints between words in the whole article based on their semantic relatedness and contextual distance. The proposed method is successfully implemented in both statistical and rule-based translators. We evaluate those systems by translating 100 articles in the EnglishWikipedia into Japanese. The results show that the ratio of appropriate word selection for common nouns increased to around 75% with our method, while it was around 55% without our method.