基于循环卷积神经网络的藏文句类识别

Tibetan Sentence Classification Method Based on Recurrent Convolutional Neural Networks

句子是语言的最小使用单位,句类识别是为了进一步细化句法和句义研究。由于藏文句尾通常没有特殊的标点符号来识别不同句类,因此这一藏文语言特性就变成了一大难题。该文提出了基于语境和功能特征为一体的句子用途分类方案。首先,该文介绍了文法中藏文句子分类及其特征。其次,收集了大量藏文句子并对其进行了人工标注。最后,采用循环卷积神经网络对藏文句类进行了自动识别。实验表明,该模型对藏文句类识别有较为显著的效果。

Sentence recognition is essential to the study of syntax and sentence meaning since there are no special punctuation marks at the end of Tibetan sentences to indicate sentence classes.In this paper,a sentence-use classification scheme is proposed based on the context and functional features of the sentences.Firstly,we introduce the classification and characteristics of Tibetan sentence classes in grammar.Secondly,we collect a large number of Tibetan sentences and manually labeled them.Finally,we use recurrent convolutional neural network to automatically identify Tibetan sentence classes.The experiment shows that the model has a significant effect on the recognition of Tibetan sentence classification.