Transformers for Classifying Fourth Amendment Elements and Factors Tests
Transformers for Classifying Fourth Amendment Elements and Factors Tests
复制标题
用于对第四修正案要素和因素测试进行分类的变压器
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Wesley M. Oliver
中科院分区：
文献类型：
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作者：
Evan William Gretok;David Langerman;Wesley M. Oliver
确定法院是否在第四修正案案件中适用了明线或全部证据规则，这表明即使对人类律师和法官来说也是一个难题。确定管辖问题的测试类型对于回答法律的问题至关重要。现代自然语言处理（NLP）工具，如transformers，展示了从未标记文本中提取相关特征的能力。本研究证明了BERT，ROBERTa，和ALBERT Transformer模型的有效性，第四修正案的情况下，由亮线或整体的规则进行分类。两种方法被认为是在其中模型的训练，无论是积极的语言提取的domainexpert或与全文的情况下。Transformers在全文上的准确率高达92.31%，进一步证明了NLP技术在特定领域任务上的能力，即使没有手工制作的功能。
Determining if a court has applied a bright-line or totality-of-thecircumstances rule for Fourth Amendment cases demonstrates a difficult problem even for human lawyers and justices. Determining the type of test that governs an issue is essential to answering a legal question. Modern natural language processing (NLP) tools, such as transformers, demonstrate the capacity to extract relevant features from unlabelled text. This study demonstrates the effectiveness of the BERT, RoBERTa, and ALBERT transformer models to classify Fourth Amendment cases by bright-line or totality-of-the-circumstances rule. Two approaches are considered in which models are trained with either positive language extracted by a domainexpert or with full texts of cases. Transformers attain up to 92.31% accuracy on full texts, further demonstrating the capability of NLP techniques on domain-specific tasks even without handcrafted features.
影响因子：
3.8
作者：
Aletras, Nikolaos;Tsarapatsanis, Dimitrios;Lampos, Vasileios
通讯作者：
Lampos, Vasileios