|
|||||
|
|
| 基于循环卷积神经网络的藏文句类识别 | |
| 引用本文： | 柔特,才让加.基于循环卷积神经网络的藏文句类识别[J].中文信息学报,2019,33(12):76-82. |
| 作者姓名： | 柔特 才让加 |
| 作者单位： | 1.青海师范大学 计算机学院,青海 西宁 810016; 2.青海省藏文信息处理与机器翻译重点实验室,青海 西宁 810008 |
| 基金项目： | 国家重点研发计划(2017YFB1402200);国家自然科学基金(61662061);国家社会科学基金(14BYY132,15BYY167,16YY167) |
| 摘 要： | 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。 |
| 关 键 词： | 藏文句类 循环卷积神经网络 词向量 句类识别 |
Tibetan Sentence Classification Method Based on Recurrent Convolutional Neural Networks |
|
| ROU Te,CAI Rangjia.Tibetan Sentence Classification Method Based on Recurrent Convolutional Neural Networks[J].Journal of Chinese Information Processing,2019,33(12):76-82. | |
| Authors: | ROU Te CAI Rangjia |
| Affiliation: | 1.School of Computer Science, Qinghai Normal University, Xining, Qinghai 810016, China; 2.Provincial Key Laboratory of Tibetan Intelligent Information Processing and Machine Translation, Xining, Qinghai 810008, China |
| Abstract: | 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. |
| Keywords: | Tibetan sentences classification recurrent convolutional neural network word vector sentence recognition |
| 点击此处可从《中文信息学报》浏览原始摘要信息 | |
| 点击此处可从《中文信息学报》下载全文 | |