Based on the importance of automatic classification of Tibetan La case example sentences in Tibetan natural language processing, according to the usage and adding rules of Tibetan La case, this paper classifies Tibetan La case example sentences and defines the classification concept, and proposes an automatic classification model of Tibetan La case example sentences with fusion dual-channel syllable features. The proposed model first uses word2vec and Glove to construct a dual-channel Tibetan syllable embedding, and combines the dual-channel syllable features in each convolution respectively to enrich the expression of input features and improve the spatial representation ability of the convolutional layer. Then in each convolution, the Bi-LSTM combined with the hierarchical attention mechanism is used to learn the timing features, and the multi-channel features are spliced to improve the learning ability of the context timing features. Finally, the automatic classification of Tibetan La case example sentences is realized through the full link layer and the Softmax layer. Experiments show that proposed model has an accuracy of 90.26% in the classification of Tibetan La case example sentences on the test set.

基于藏文La格例句的自动分类在藏语自然语言处理领域的重要性, 根据藏文La格的用法和添接规则, 在对藏文La格例句进行分类并定义分类概念的基础上, 提出一种融合双通道音节特征的藏文La格例句自动分类模型。该模型首先使用word2vec和 Glove构建双通道藏文音节嵌入, 分别在每路卷积中融合双通道音节特征, 丰富输入特征的表达和提高卷积层的空间表征能力; 然后在每一路卷积均使用结合层级注意力机制的Bi-LSTM学习时序特征后, 拼接多路特征, 提高上下文时序特征的学习能力; 最后通过全链接层和Softmax层实现藏文La格例句自动分类。实验结果表明, 该模型在测试集上的藏文La格例句分类准确率达到 90.26%。