Transformers for classifying fourth amendment elements and factors tests
Frontiers in Artificial Intelligence and Applications
Bright-line rule, Elements, Factors, Fourth amendment, Text classification, Totality-of-the-circumstances, Transformers
Determining if a court has applied a bright-line or totality-of-the-circumstances 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 domain-expert 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.
Gretok, E., Langerman, D., & Oliver, W. (2020). Transformers for classifying fourth amendment elements and factors tests. Frontiers in Artificial Intelligence and Applications, 334, 63-72. https://doi.org/10.3233/FAIA200850