基于深度学习的法律文书识别方法研究

doi:10.16180/j.cnki.issn1007-7820.2019.12.018

Abstract

Abstract:

In order to improve the construction efficiency of the knowledge base of digital legal documents, this paper proposed a method of legal document recognition based on the theory of deep learning. This method constructed deep neural network based on long and short term memory (LSTM) network element structure, and realized entity recognition in natural language. After testing, this method could effectively extract the names of people, places and institutions in legal documents. In the test set used in this paper, compared with the CRFs algorithm, this method could achieve about 10% improvement in accuracy, recall and F.

Key words: legal documents, natural language processing, deep learning, entity recognition, LSTM, CRFs

CLC Number:

TN911.7

MENG Xin. Research on Recognition Method of Legal Documents Based on Deep Learning[J].Electronic Science and Technology, 2019, 32(12): 84-86.

Figures/Tables 6

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References 10

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参数描述	参数值
词向量维度	100
LSTM层输出维度	128
全连接层输出维度	64
Batch层	32

算法	P	R	F
CRFs	98.92%	94.53%	96.67
LSTM	99.30%	94.60%	96.89

算法	P	R	F
CRFs	79.52%	65.90%	72.09
LSTM	92.58%	74.77%	82.73

算法	P	R	F
CRFs	75.4%	68.23%	72.21
LSTM	87.48%	76.50%	81.62

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Research on Recognition Method of Legal Documents Based on Deep Learning

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