Identifying word correspondence in parallel texts
HLT '91 Proceedings of the workshop on Speech and Natural Language
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Combining clues for word alignment
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Chinese-Korean word alignment based on linguistic comparison
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
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Word alignment using recency-vector based approach has recently become popular. One major advantage of these techniques is that unlike other approaches they perform well even if the size of the parallel corpora is small. This makes these algorithms worth-studying for languages where resources are scarce. In this work we studied the performance of two very popular recency-vector based approaches, proposed in (Fung and McKeown, 1994) and (Somers, 1998), respectively, for word alignment in English-Hindi parallel corpus. But performance of the above algorithms was not found to be satisfactory. However, subsequent addition of some new constraints improved the performance of the recency-vector based alignment technique significantly for the said corpus. The present paper discusses the new version of the algorithm and its performance in detail.