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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (7): 10-25    DOI: 10.11925/infotech.2096-3467.2020.1230
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Identifying Multi-Type Entities in Legal Judgments with Text Representation and Feature Generation
Wang Hao,Lin Kerou(),Meng Zhen,Li Xinlei
School of Information Management, Nanjing University, Nanjing 210023, China
Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China
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Abstract  

[Objective] This paper investigates the performance of entity recognition models for legal judgments, aiming to construct better legal knowledge base in the future. [Methods] First, we extracted the court trial process and court opinions from criminal judgment texts to build an experimental dataset. Then, we compared the entity recognition results of the CRFs model (with artificially constructed features), the IDCNN-CRFs model (with automatically generated features), and the BiLSTM-CRFs model. Both of the IDCNN-CRFs and BiLSTM-CRFs models used pre-trained word vectors for their char embedding. The models’ transferred abilities on other types of legal judgment texts were also compared. [Results] The ALBERT-BiLSTM-CRFs model had the best recognition performance. Its F1 micro-average value reached 95.28%. However, the training time of the IDCNN-CRFs model was about 1/6 of the ALBERT-BiLSTM-CRFs model. Both models had good transferred abilities. [Limitations] Most of the recognized entities were the general ones. More domain-related entities are needed in future studies to enhance the model’s practical value. [Conclusions] The ALBERT-BiLSTM-CRFs and IDCNN-CRFs models could more effectively recognize entities from legal judgments and show better transferred ability than the CRFs model.

Key wordsLegal Judgments      Feature Generation      CRFs      IDCNN-CRFs      ALBERT-BiLSTM-CRFs     
Received: 07 December 2020      Published: 11 August 2021
ZTFLH:  TP393  
Fund:National Natural Science Foundation of China(72074108);Youth Interdisciplinary Team of Liberal Arts in Nanjing University(2020300093);Jiangsu Young Talents in Social Sciences;Tang Scholar of Nanjing University
Corresponding Authors: Lin Kerou,ORCID:0000-0003-0026-8771     E-mail: keroulin@foxmail.com

Cite this article:

Wang Hao, Lin Kerou, Meng Zhen, Li Xinlei. Identifying Multi-Type Entities in Legal Judgments with Text Representation and Feature Generation. Data Analysis and Knowledge Discovery, 2021, 5(7): 10-25.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.1230     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I7/10

Research Framework
序号 实体类别 标签 实例
1 罪名 Crime 交通肇事罪
2 人名 Per 张××、程某甲
3 地名 Loc 临清市、永馆路
4 机构名 Org 淮安区人民检察院、淮安市公安局淮安分局物证鉴定室
5 日期 Date 2016年2月11日
6 时间 Time 上午10时30分、晚
Entity Type and Corresponding Examples
序号 实体类别 训练集 测试集
Token数量 实体数量(不去重) 实体数量(去重) Token数量 实体数量(不去重) 实体数量(去重)
1 Crime 3 449 742 58 400 84 17
2 Date 15 560 1 815 977 1 420 171 127
3 Loc 9 964 3 019 1 264 611 198 110
4 Org 15 612 1 888 994 1 412 169 107
5 Per 33 708 12 625 1 348 2 942 1 137 194
6 Time 2 825 831 216 205 67 30
7 O 307 167 25 028
Number of Entities and Token in Training Set and Test Set
The Proportion of Non-Repetitive Entities in Training Set and Test Set
The Proportion of New Entities in Test Set
序号 标签 意义 实例
1 B 实体的开头 “晚”字和“豫”字
2 I 实体除开头外的其他字符 “上9时30分”
3 O 非实体的字 其他字
Explanation of the BIO Label System
序号 标签 意义 实例
1 B 实体的开头 “晚”字
2 I 实体的中间部分 “上9时30”
3 E 实体的结尾 “分”字
4 S 单个字组成的实体 “豫”字
5 O 非实体的字 其他字
Explanation of the BIEOS Label System
Design of CRFs Model
IDCNN-CRFs Model Structure
序号 参数 默认值
1 Batch Size 32
2 Epoch 100
3 dropout_keep 0.5
4 字嵌入维度 100
5 Filter数量 100
6 Gradient Clip 5
7 学习率 0.001
8 优化器 Adam
IDCNN-CRFs Model Default Parameters
Structure of BiLSTM-CRFs Embedded by Random Numbers and BERT-BiLSTM-CRFs
序号 参数 默认值
1 Batch Size 64
2 Epoch 100
3 LSTM层Units 128
4 LSTM层return_sequences TRUE
5 全连接层Units 64
6 全连接层激活函数 tanh
BiLSTM Model Default Parameters
ALBERT-BiLSTM-CRFs Model Structure
序号 实体类别 BIO BIEOS
P R F 1 P R F 1
1 Crime 98.80% 97.62% 98.20% 98.78% 96.43% 97.59%
2 Date 97.48% 90.64% 93.94% 92.55% 87.13% 89.76%
3 Loc 81.28% 76.77% 78.96% 75.89% 68.55% 72.03%
4 Org 89.13% 72.78% 80.13% 87.32% 73.37% 79.74%
5 Per 96.03% 93.58% 94.79% 94.75% 92.00% 93.35%
6 Time 100.00% 89.55% 94.49% 98.46% 95.52% 96.97%
Experimental Results of Different Label Systems
The F1 Values of Different Feature Experiments on CRFs Model
The F1 Values of Different Batch Size Experiments on IDCNN-CRFs Model When Epoch=1
The F1 Values of Different Batch Size Experiments on IDCNN-CRFs Model When Epoch=10
序号 实体类别 Epoch = 1 Epoch = 10 Epoch = 20
1 Crime 48.10% 100.00% 97.62%
2 Date 85.96% 93.29% 96.49%
3 Loc 68.85% 85.20% 84.16%
4 Org 43.48% 81.85% 77.91%
5 Per 93.34% 95.08% 95.47%
6 Time 76.39% 99.25% 94.49%
The F1 Values of Different Epoch Experiments on IDCNN-CRFs Model When Bath Size=8
序号 实体类别 Dropout = 0.4 Dropout = 0.5 Dropout = 0.6 Dropout = 0.7 Dropout = 0.8
1 Crime 98.80% 100.00% 99.40% 100.00% 97.59%
2 Date 93.49% 93.29% 93.53% 93.49% 94.15%
3 Loc 84.97% 85.20% 86.15% 87.11% 85.27%
4 Org 78.79% 81.85% 81.93% 81.08% 79.17%
5 Per 94.08% 95.08% 95.02% 95.78% 96.27%
6 Time 97.71% 99.25% 96.97% 94.49% 92.19%
The F1 Values of Different Dropout Experiments on IDCNN-CRFs Model When Batch Size=8 and Epoch=10
序号 实体类别 Epoch = 1 Epoch = 3 Epoch = 5
1 Crime 81.25% 96.89% 97.44%
2 Date 86.53% 92.04% 94.05%
3 Loc 75.63% 76.59% 80.20%
4 Org 67.68% 75.57% 77.17%
5 Per 93.29% 92.79% 94.43%
6 Time 95.52% 98.49% 99.26%
7 Micro Avg 88.38% 89.78% 91.58%
The F1 Values of Different Epoch Experiments on BiLSTM-CRFs Model Embedded by Random Numbers
序号 Epoch loss acc val _ loss val _ acc
1 1 11.0966 97.56% 209.3262 98.33%
2 2 1.9536 99.46% 198.5077 98.44%
3 3 1.1717 99.63% 188.8806 98.16%
4 4 0.8223 99.72% 178.6167 98.44%
5 5 0.5982 99.78% 170.9082 98.19%
The Change of loss and acc During the Iteration When Epoch=5
序号 Epoch loss acc val _ loss val _ acc
1 1 11.6478 97.39% 202.4137 98.93%
2 2 1.9299 99.42% 192.3672 99.12%
3 3 1.1491 99.61% 183.2427 99.16%
The Change of loss and acc During the Iteration When Epoch=3
序号 实体类别 字嵌入方式
随机数 BERT ALBERT
1 Crime 96.89% 100.00% 100.00%
2 Date 92.04% 91.01% 93.53%
3 Loc 76.59% 88.38% 87.28%
4 Org 75.57% 80.97% 83.54%
5 Per 92.79% 99.02% 98.48%
6 Time 98.49% 91.18% 99.25%
7 Micro Avg 89.78% 95.17% 95.28%
The F1 Values of Different Word Embedding Experiments on BiLSTM-CRFs Model
The F1 Values of the Three Types of Models
序号 模型 结果
类型
实体数量
Crime Date Loc Org Per Time
1 CRFs TP + FP 1 8 16 6 30 1
TP 1 8 14 3 29 1
2 IDCNN-CRFs TP + FP 3 8 15 8 32 1
TP 3 8 14 4 31 1
3 ALBERT-BiLSTM-CRFs TP + FP 3 8 17 5 36 3
TP 3 8 15 4 26 1
Total 7 8 19 5 32 1
Comparison of Experimental Results of New Data on Different Models
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