Jisuanji kexue (Oct 2021)

Entity Recognition Fusing BERT and Memory Networks

  • CHEN De, SONG Hua-zhu, ZHANG Juan, ZHOU Hong-lin

DOI
https://doi.org/10.11896/jsjkx.200900015
Journal volume & issue
Vol. 48, no. 10
pp. 91 – 97

Abstract

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Entity recognition is a sub task of information extraction.The traditional entity recognition model is used to identify entities of personnel,organization,location and name.In the real world,more types of entities must be considered,and fine-grained entity recognition is needed.At the same time,traditional entity recognition models such as BiGRU cannot make full use of the global features in a wider range.This paper presents an entity recognition model based on memory network and BERT.The pre-training language model of BERT is used for better semantic representation,and the memory network module can memorize a wider range of features.The results of entity recognition for cement clinker production corpus data show that this method can re-cognize entities and has some advantages over other traditional models.In order to further verify the model in this paper,experiments are carried out on the CLUENER2020 dataset.The results show that the optimization based on BiGRU-CRF model using BERT and memory network module can improve the effect of entity recognition.

Keywords