Alignment model adaptation for domain-specific word alignment

Wu Hua, Wang Haifeng, Liu Zhanyi
2005 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL '05  
This paper proposes an alignment adaptation approach to improve domain-specific (in-domain) word alignment. The basic idea of alignment adaptation is to use out-of-domain corpus to improve in-domain word alignment results. In this paper, we first train two statistical word alignment models with the large-scale out-of-domain corpus and the small-scale in-domain corpus respectively, and then interpolate these two models to improve the domain-specific word alignment. Experimental results show that
more » ... l results show that our approach improves domain-specific word alignment in terms of both precision and recall, achieving a relative error rate reduction of 6.56% as compared with the state-of-the-art technologies.
doi:10.3115/1219840.1219898 dblp:conf/acl/WuWL05 fatcat:5yrwkiryc5f5ffh7h3nhlhmc44