深度学习模型的藏文人名识别方法

Recognition of Tibetan personal name based on deep learning

文章采用深度学习技术探讨了藏文文本中的人名识别方法。首先通过word2vec训练出藏文词向量,然后在该词向量的基础上利用神经网络探讨了藏文人名识别技术,通过实验证明识别效果F1能够达到94%以上。训练出了比较好的藏文词向量,且结合藏文特点设计了检测藏文词向量好坏的方法,并采用了前向传播和随机梯度下降算法,经过多组实验验证了藏文人名识别效果。

In this paper, we discussed a method which can be used to recognize Tibetan Personal Name by using deep learning technique. First, Word2vec was used to train the Tibetan word embedding, and then neural net-work was used to distinguish Tibetan Personal Name based on the trained word embedding. Our tests proved that the efficiency of recognition （i.e. F1） can be better than 94%. In this paper, a good Tibetan word embedding is trained, and combining the characteristics of Tibetan word, we discussed a method which can be used to evaluate Tibetan word embedding. Furthermore, the efficiency of recognition of Tibetan Personal Name were tested using forward propagation and the stochastic gradient descent algorithm.