藏文紧缩格识别方法

分词是自然语言处理的一项基础性工作，对自然语言处理的后继工作有较大的影响。紧缩格的识别是藏文分词中最难最重要的技术之一。通过剖析已有藏文紧缩词识别方法，分析藏文字词的特征，针对性地提出了识别藏文紧缩格的规则算法、添加—还原算法和最大熵模型的特征模板，从而得到基于规则、添加还原法与最大熵模型相结合的藏文紧缩格识别方法。实验数据表明，该方法识别藏文紧缩格的准确率、召回率和F1值分别达99.26%、96.47%、97.85%，比现有最高的准确率有了较明显的提高。

Recognition method of Tibetan abbreviated case-auxiliary words

Word segmentation is a basic work of natural language processing, which has a great influence on the subsequent work of it, the recognition of abbreviated case-auxiliary words is one of the most difficult and important technologies of Tibetan word segmentation. Through dissecting the existing recognition methods of abbreviated case-auxiliary words, this paper analyzed the characteristics of Tibetan words, targetedly proposed recognition algorithm of Tibetan abbreviated case-auxiliary words rules, add - restore algorithm and the maximum entropy models feature template, then the methods of recognizing abbreviated case-auxiliary words based on the rules, add-restore methods and the maximum entropy model were obtained. The experimental data showed that the accuracy, recall rate and F value of the method is 99.26%, 96.47%, and 97.85% respectively , which shows a obvious progress than that of the existing methods.