融合多种使用词信息方法的命名实体识别研究

Research on Named Entity Recognition Based on Multiple Words Used Information Methods

文章对融合词信息增强中文命名实体识别问题进行了研究,提出一种用于中文命名实体识别的融合词信息神经网络模型系统。首先使用预训练语言模型Bert对字进行编码得到字标识,然后使用SoftLexicon基于统计的方法将词统计语义信息融合进入字表示中,之后使用设计的GraphLexicon根据文本内字、词之间的交互关系图结构,将字词信息表示相互融合,达到较高的命名实体识别准确率。

In this paper,the problem of enhancing Chinese named entity recognition by fusing word information is studied,and a neural network model system based on fusing word information for Chinese named entity recognition is proposed.First,the pre training language model Bert is used to encode the character to get the character identification,and then the statistic based approach SoftLexicon is used to fuse the word statistical semantic information into the character representation.Then,according to the structure of the interaction graph between characters and words in the text,the character and word information representation are fused to achieve a high accuracy of named entity recognition.