A statistical approach to machine translation
Computational Linguistics
Identifying word correspondence in parallel texts
HLT '91 Proceedings of the workshop on Speech and Natural Language
A comparison of indexing techniques for Japanese text retrieval
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
A program for aligning sentences in bilingual corpora
Computational Linguistics - Special issue on using large corpora: I
Computational Linguistics - Special issue on using large corpora: I
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Structural matching of parallel texts
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Bilingual text, matching using bilingual dictionary and statistics
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
A rule-based approach to prepositional phrase attachment disambiguation
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Towards automatic extraction of monolingual and bilingual terminology
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Maximum likelihood alignment of translation equivalents
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
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In this paper, we propose an algorithm for aligning words with their translation in a bilingual corpus. Conventional algorithms are based on word-by-word models which require bilingual data with hundreds of thousand sentences for training. By using a word-based approach, less frequent words or words with diverse translations generally do not have statistically significant evidence for confident alignment. Consequently, incomplete or incorrect alignments occur. Our algorithm attempts to handle the problem using class-based rules which are automatic acquired from bilingual materials such as a bilingual corpus or machine readable dictionary. The procedures for acquiring these rules is also described. We found that the algorithm can align over 80% of word pairs while maintaining a comparably high precision rate, even when a small corpus was used in training. The algorithm also poses the advantage of producing a tagged corpus for word sense disambiguation.