Research on Chinese named entity recognition based on feature fusion

Abstract

Its core goal is to extract entities with specific meanings from text, and classify and label them. Recognizing entities in text can provide support for downstream tasks such as knowledge graphs, machine translation, and question answering systems, and therefore has important practical significance and research value. At present, the deep learning technology is used to classify Chinese names, and a satisfactory result is obtained. However, due to the characteristics and complexity of Chinese semantics, the current entity recognition methods of Chinese names still have some problems, such as the entity boundary is difficult to be divided, the structure is flexible, and the entity is easy to be confused. This project intends to start with the extraction of multi-source information of Chinese name entities, build semantic information based on multi-source semantic information, and build a semantic entity model based on feature fusion. Therefore, this paper proposes a method of Chinese name ontology recognition based on semantic analysis. Based on this, this paper conducts research on Chinese named entity recognition technology that uses multiple features for semantic enhancement. First, the research background and current situation of named entity recognition are analyzed and the various basic neural networks widely used in Chinese named entity recognition algorithms and the basic structure of mainstream models are introduced. Secondly, the entity boundaries faced by current Chinese named entity recognition are difficult to divide., flexible word formation, entity ambiguity and other difficulties, two Chinese named entity recognition models based on external semantic information enhancement are proposed to solve the above-mentioned problems existing in the Chinese named entity recognition task and further improve the three different models of MSRA, Resume and Weibo.