结合单词-字符引导注意力网络的中文旅游文本命名实体识别

Named Entity Recognition for Chinese Tourism Texts Combining Word-Character Guided Attention Network

传统基于词向量表示的命名实体识别方法通常忽略了字符语义信息、字符间的位置信息,以及字符和单词间的关联关系。提出一种基于单词-字符引导注意力网络(WCGAN)的中文旅游命名实体识别方法,利用单词引导注意力网络获取单词间的序列信息和关键单词信息,采用字符引导注意力网络捕获字符语义信息和字符间的位置信息,增强单词和字符间的关联性与互补性,从而实现中文旅游文本中命名实体的识别。实验结果表明,WCGAN方法在ResumeNER和TourismNER基准数据集上的F值分别为93.491%和92.860%,相比Bi-LSTM+CRF、Char-Dense等方法识别效果更好。

The traditional Named Entity Recognition(NER) methods based on word vector usually neglect the character semantics of Chinese characters,the position information between characters,and the dependence between characters and words. To address the problem,this paper proposes a NER method based on Word-Character Guided Attention Network(WCGAN) for Chinese tourism texts. The method uses the Word-Guided Attention Network(WGAN)to obtain the sequence information between words and further capture the significant word information.The Character Guided Attention Network(CGAN)is used to obtain the information about character semantics and position between characters,and thus enhance the relevance and complementarity between words and characters to realize the recognition of named entities in Chinese tourism texts. Experimental results on the two benchmark datasets of ResumeNER and TourismNER show that the F values of the WCGAN method are 93.491% and92.860% respectively,and the proposed method has better recognition performance than Bi-LSTM+CRF,Char-Dense and other methods.