摘要
This paper claries the definition of alignment from the viewpoint of linguistic similarity. Many alignment algorithms have been proposed with very high precision. But the languages belong to occidental family. We propose a new method for alignment between languages that do not belong to the same language family. On the contrary to most of the previously proposed methods that rely heavily on statistics, our method attempts to use linguistic knowledge to overcome the problems of statistical model. Experimental results confirm that the algorithm can align over 85 % of word pairs while maintaining a comparably high precision rate, even when a small corpus is used in training.
This paper claries the definition of alignment from the viewpoint of linguistic similarity. Many alignment algorithms have been proposed with very high precision. But the languages belong to occidental family. We propose a new method for alignment between languages that do not belong to the same language family. On the contrary to most of the previously proposed methods that rely heavily on statistics, our method attempts to use linguistic knowledge to overcome the problems of statistical model. Experimental results confirm that the algorithm can align over 85% of word pairs while maintaining a comparably high precision rate, even when a small corpus is used in training.
出处
《计算机科学》
CSCD
北大核心
2002年第11期44-47,共4页
Computer Science
基金
国家自然科学基金
