MAF-CNER : A Chinese Named Entity Recognition Model Based on Multifeature Adaptive Fusion

Complexity 2021:1-9 (2021)
  Copy   BIBTEX

Abstract

Named entity recognition is a subtask in natural language processing, and its accuracy greatly affects the effectiveness of downstream tasks. Aiming at the problem of insufficient expression of potential Chinese features in named entity recognition tasks, this paper proposes a multifeature adaptive fusion Chinese named entity recognition model. The model uses bidirectional long short-term memory neural network to extract stroke and radical features and adopts a weighted concatenation method to fuse two sets of features adaptively. This method can better integrate the two sets of features, thereby improving the model entity recognition ability. In order to fully test the entity recognition performance of this model, we compared the basic model and other mainstream models on Microsoft Research Asia and “China People’s Daily” dataset from January to June 1998. Experimental results show that this model is better than other models, with F1 values of 97.01% and 96.78%, respectively.

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 76,297

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Similar books and articles

Analytics

Added to PP
2021-02-10

Downloads
5 (#1,169,683)

6 months
1 (#450,425)

Historical graph of downloads
How can I increase my downloads?