基于机器翻译的维吾尔语形态分析研究

针对现有维吾尔语形态分析研究中存在的数据稀疏、模型构建复杂等问题，提出一种基于机器翻译的维吾尔语形态分析模型，即将维吾尔语词干提取（词性标注）任务中词干提取前（词性标注前）的句子看作是机器翻译模型训练过程中的源语言端，词干提取后（词性标注后）的句子看作是目标语言端；为了达到最佳的效果，加入了外部信息模块和联合校验模块以优化模型。实验结果表明，基于机器翻译框架的维吾尔语形态分析模型在词干提取、词性标注两个任务上优于其他模型。对比英语（词干提取、词性标注）、汉语（分词、词性标注）实验结果，提出的方法更适合维吾尔语形态分析。

Research on machine translation based Uyghur morphological analysis

To alleviate the data sparseness and reduce the complexity of models construction in Uyghur morphology analysis, it proposes a Statistical Machine Translation （SMT） based morphology analysis model, which considers the pre-stem words （pre-Part-Of-Speech （POS） tagging） in Uyghur word stemming （POS tagging） as the source part of SMT system, and the post-stemming words  （POS tags） as the target part. To optimize the model, it uses dictionaries and joint validation in the model. Experimental results show that, the approach outperforms other systems in Uyghur word stemming and part-of-speech tagging. Compared with segmentation and POS tagging tasks in English and Chinese, the approach is more suitable for Uyghur.