Chinese named entity recognition incorporating syntactic information

Abstract

At present, Chinese named entity recognition techniques incorporating lexical knowledge have gained good development on graph neural networks. However, since there is still a deficiency in graph neural networks for longer distance semantic information and its pointing and localization information. In this task, we try to extract the missing pointing and locating information by employing relative position encoding techniques; it deals with long-distance dependencies by obtaining lexical information through dependency parsing. It is also able to deal more effectively with long-distribution relations and missing data in terms of direction and location through interactions with words in relative location coding. The experimental results on three NER datasets show that the proposed model is improved compared with other governor models.