Classifying Crime Types using Judgment Documents from Social Media
- URL: http://arxiv.org/abs/2306.17020v2
- Date: Sat, 21 Oct 2023 14:44:41 GMT
- Title: Classifying Crime Types using Judgment Documents from Social Media
- Authors: Haoxuan Xu, Zeyu He, Mengfan Shen, Songning Lai, Ziqiang Han and Yifan
Peng
- Abstract summary: The task of determining crime types based on criminal behavior facts has become a very important and meaningful task in social science.
The data samples themselves are unevenly distributed, due to the nature of the crime itself.
This article proposes a new training model to solve this problem through NLP processing methods.
- Score: 11.16381622758947
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The task of determining crime types based on criminal behavior facts has
become a very important and meaningful task in social science. But the problem
facing the field now is that the data samples themselves are unevenly
distributed, due to the nature of the crime itself. At the same time, data sets
in the judicial field are less publicly available, and it is not practical to
produce large data sets for direct training. This article proposes a new
training model to solve this problem through NLP processing methods. We first
propose a Crime Fact Data Preprocessing Module (CFDPM), which can balance the
defects of uneven data set distribution by generating new samples. Then we use
a large open source dataset (CAIL-big) as our pretraining dataset and a small
dataset collected by ourselves for Fine-tuning, giving it good generalization
ability to unfamiliar small datasets. At the same time, we use the improved
Bert model with dynamic masking to improve the model. Experiments show that the
proposed method achieves state-of-the-art results on the present dataset. At
the same time, the effectiveness of module CFDPM is proved by experiments. This
article provides a valuable methodology contribution for classifying social
science texts such as criminal behaviors. Extensive experiments on public
benchmarks show that the proposed method achieves new state-of-the-art results.
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