字符级的维吾尔语形态协同分析方法

针对维吾尔语中构形词缀种类多、构形复杂以及发生音变现象等问题, 提出一种基于字符级的维吾尔语形态协同分析方法。该方法最大的特点是同时进行维吾尔语的形态切分、形态标注以及音变还原, 将词素边界、形态标记以及音变信息用一个复合标记描述, 采用字符序列的标注方法进行训练。实验结果显示, 形态切分、形态标注及音变还原的正确率分别达到96.39%, 92.78%和99.79%, 系统总体正确率达92.59%。

Collaborative Analysis of Uyghur Morphology Based on Character Level

The Uyghur language has various inflectional affixes, complex structures and phonetic changes. The authors propose a collaborative analysis method for Uyghur morphology at character level. It includes three procedures: morpheme segmentation, morphological annotation and reduction of phonetic changes. The main characteristics of this method is to use a composite tag to represent the morpheme boundaries, annotations and phonetic changes. In addition, character sequence annotation is used to train the model. Experimental results show that the accurency of morpheme segmentation, morphological annotation and reduction of phonetic reaches 96.39%, 92.78% and 99.79% respectively. The overall accuracy of the system reaches 92.59%.