Improved transformation based part of speech tagging of Latin Mongolian
改进的基于转换方法的拉丁蒙文词性标注

To solve the problem of rule learning time cost for traditional transformation based part of speech tagging method of Latin Mongolian, a dynamic partition algorithm was presented. It used rule conflict and rule dependency to dynamically partition the training corpus, reduced the searching space and increased the rule learning speed. In an open test of a Latin Mongolian corpus with 10000 sentences, the time that new algorithm cost was only 32% of the old one.