The Advance of Deep Learning Based Named Entity Recognition

Abstract

Named Entity Recognition is a well-known research direction in the area of deep learning. It takes an essential role in natural language processing. The goal of the Named Entity Recognition is to identify and separate the named entities, such as a person, location, or name, from the entire text. In addition, the deep learning model has achieved remarkable achievements in many other areas, and the deep learning-based named entity recognition method has reached an F score of over 90. The paper summarizes the development of named entity recognition and puts forward a recurrent neural network-based named entity recognition algorithm. The result shows that improving the performance of the Named Entity Recognition model by simply enriching the number and variety of the input datasets or providing the model with substantial computing resources for training is nearly impossible without a significant breakthrough. There still requires another way to improve the NER model in future research.