一个基于标准的分类模型,使用增强和对比学习来分析不平衡的语句数据
Junho Shin1, Jinhee Kwak1, Jaehee Jung1
1Department of Information and Communication Engineering, University of Myongji, Yongin, Gyeonggi-do, South Korea.
Heliyon
|July 18, 2024
概括
这项研究开发了一种使用NLP的基于标准的内容分析 (CBCA) 的客观模型,大大减少了受害者陈述分析中的人类主观性,以提高法律准确性.
科学领域:
- 法医心理学 法医心理学
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 基于标准的内容分析 (CBCA) 对于评估受害者的陈述真实性至关重要.
- 人类分析中的主观性可能会影响证词评估的可靠性.
- 现有的CBCA方法面临着数据分布不平衡的挑战.
研究的目的:
- 为CBCA语句分析开发一个客观的,基于自然语言处理 (NLP) 的分类模型.
- 通过最小化人类主观性来提高CBCA的准确性和可靠性.
- 解决标准分类中的数据不平衡问题.
主要方法:
- 利用NLP技术,为CBCA创建一个客观的分类模型.
- 采用数据增强和双对比学习来微调罗伯塔语言模型.
- 应用基于模型的优化,用于超参数调整,以最大限度地提高分类性能.
主要成果:
- 在宏观F1比人类分类得分提高了8.5%.
- 与以前的基准标准相比,宏观F1得分提高了24%,准确性增加了13%.
- 该模型有效地减少了语句分析中的人类主观性.
结论:
- 拟议的NLP模型提供了一种更客观,更可靠的方法来评估受害者陈述的可信性.
- 这一进步对法律诉讼和刑事调查产生了重大影响.
- 减少主观性可以提高判决的准确性,并支持司法公正.
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