Named Entity Recognition Model of Telecommunication Network Fraud Crime Based on ELECTRA-CRF

doi:10.3969/j.issn.1671-1122.2021.06.008

Abstract

Abstract:

This paper proposes a text named entity recognition model of telecommunication network fraud crimes based on ELECTRA-CRF. Firstly, the annotated corpus is input into ELECTRA model to obtain the state transition features with Chinese characters as granularity. And then CRF model is used to calculate the transfer score to determine the entity label group of the character at the current position and its adjacent position. Finally, the BERT-CRF model and RoBERTa-CRF model are compared through experiments. The experimental results show that the text named entity recognition model proposed in this paper based on ELECTRA-CRF is significantly better than the other two deep learning models in operation efficiency, and the loss of the accuracy, recall rate and reconciliation average are very small. It can be well applied to the named entity recognition of telecommunication network fraud crimes.

Key words: named entity recognition, ELECTRA model, telecommunication network fraud crime

CLC Number:

TP309

DING Jiawei, LIU Xiaodong. Named Entity Recognition Model of Telecommunication Network Fraud Crime Based on ELECTRA-CRF[J]. Netinfo Security, 2021, 21(6): 63-69.

Figures/Tables 7

References 16

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命名实体类	数量/个	高频实体
ContactWay	1020	手机、电话、微信
ContactNum	265	无
Identity	1392	客服、工作人员
Trick	1382	六合彩、安全账户
Payment	1020	银行转账、支付宝
PayName	592	无
Amount	1352	无

类别	具体信息
操作系统	Windows 10
CPU	AMD Ryzen 7 4800H
RAM	16 GB
TensorFlow	1.14
Python	3.7.8

模型实体类	ELECTRA-CRF			BERT-CRF			RoBERTa-CRF
模型实体类	P	R	F	P	R	F	P	R	F
ContactWay	0.85	0.89	0.87	0.90	0.94	0.92	0.86	0.97	0.91
ContactNum	0.76	0.95	0.84	0.86	0.95	0.90	0.90	0.95	0.93
Identity	0.85	0.91	0.88	0.81	0.90	0.85	0.76	0.87	0.81
Trick	0.66	0.85	0.75	0.71	0.88	0.79	0.66	0.87	0.75
Payment	0.70	0.85	0.77	0.67	0.87	0.75	0.57	0.74	0.64
Amount	0.95	0.99	0.97	0.94	0.99	0.96	0.95	0.99	0.97
PayName	0.97	0.97	0.97	0.98	0.98	0.98	0.99	0.97	0.98
平均值	0.80	0.90	0.85	0.80	0.92	0.86	0.77	0.90	0.83