中文摘要：

指代消解是自然语言处理技术的核心问题,该文结合维吾尔语语义特征,提出基于深度学习的维吾尔语人称代词指代消解方法。通过堆叠多层无监督RBM网络和一层有监督BP网络,构建DBN深度神经网络学习模型,RBM网络保证特征向量映射达到最优,BP网络对RBM网络的输出向量进行分类,实现维吾尔语人称代词指代消解。经过维吾尔语指代消解语料库测试,F值达到83.81%,比SVM方法高出2.88%。实验结果表明,同等条件下,该方法能有效提升维吾尔语人称代词消解的精度,有助于维吾尔语指代消解研究。

英文摘要：

Coreference resolution is a fundamental issue in natural language processing.Combining the semantic features of Uyghur,a method of Uyghur pronominal anaphora resolution based on Deep Learning is proposed.The proposed DBN（Deep Belief Nets）learning model is composed of several unsupervised RBM networks and a supervised BP network.The RBM layers preserve information as much as possible when feature vectors are mapped to next layer.The BP layer is able to classify the vector output by the last RBM layer.Then the model can be used to implement Uyghur pronominal anaphora resolution.Experiments on Uyghur coreference resolution corpus achieve 83.81%in F-score,2.88% higher than SVM.