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Mapping the Geometry of Law Using Natural Language Processing

Judicial documents and judgments are a rich source of information about legal cases, litigants, and judicial decision-makers. Natural language processing (NLP) based approaches have recently received much attention for their ability to decipher implicit information from text. NLP researchers have successfully developed data-driven representations of text using dense vectors that encode the relations between those objects. In this study, we explore the application of the Doc2Vec model to legal language to understand judicial reasoning and identify implicit patterns in judgments and judges. In an application to federal appellate courts, we show that these vectors encode information that distinguishes courts in time and legal topics. We use Doc2Vec document embeddings to study the patterns and train a classifier model to predict cases with a high chance of being appealed at the Supreme Court of the United States (SCOTUS). There are no existing benchmarks, and we present the first results

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Sandeep Bhupatiraju · · 1 min read · 14 views

Judicial documents and judgments are a rich source of information about legal cases, litigants, and judicial decision-makers. Natural language processing (NLP) based approaches have recently received much attention for their ability to decipher implicit information from text. NLP researchers have successfully developed data-driven representations of text using dense vectors that encode the relations between those objects. In this study, we explore the application of the Doc2Vec model to legal language to understand judicial reasoning and identify implicit patterns in judgments and judges. In an application to federal appellate courts, we show that these vectors encode information that distinguishes courts in time and legal topics. We use Doc2Vec document embeddings to study the patterns and train a classifier model to predict cases with a high chance of being appealed at the Supreme Court of the United States (SCOTUS). There are no existing benchmarks, and we present the first results at this task at scale. Furthermore, we analyze generic writing/judgment patterns of prominent judges using deep learning-based autoencoder models. Overall, we observe that Doc2Vec document embeddings capture important legal information and are helpful in downstream tasks.

Executive Summary

The article 'Mapping the Geometry of Law Using Natural Language Processing' explores the application of NLP techniques, specifically the Doc2Vec model, to analyze judicial documents and judgments. The study demonstrates that document embeddings can capture implicit patterns in legal texts, distinguishing courts over time and by legal topics. The authors train a classifier to predict cases likely to be appealed to the Supreme Court of the United States (SCOTUS) and analyze the writing patterns of prominent judges using autoencoder models. The findings suggest that NLP can provide valuable insights into judicial reasoning and decision-making processes.

Key Points

  • ▸ Application of Doc2Vec model to legal language to understand judicial reasoning.
  • ▸ Document embeddings distinguish courts in time and legal topics.
  • ▸ First large-scale results in predicting cases likely to be appealed to SCOTUS.
  • ▸ Analysis of writing patterns of prominent judges using autoencoder models.

Merits

Innovative Approach

The study introduces a novel application of NLP techniques to legal texts, providing a data-driven approach to understanding judicial reasoning and decision-making.

Scalability

The methods presented are scalable and can be applied to large datasets, offering potential for broader legal research and analysis.

Predictive Capability

The classifier model for predicting SCOTUS appeals demonstrates the practical utility of NLP in legal forecasting.

Demerits

Lack of Benchmarks

The absence of existing benchmarks for predicting SCOTUS appeals makes it challenging to evaluate the model's performance against established standards.

Generalizability

The study focuses on federal appellate courts, and the generalizability of the findings to other jurisdictions or legal systems may be limited.

Data Quality

The effectiveness of the NLP models is highly dependent on the quality and consistency of the judicial documents used for training.

Expert Commentary

The study 'Mapping the Geometry of Law Using Natural Language Processing' represents a significant advancement in the application of NLP techniques to legal research. By leveraging the Doc2Vec model and autoencoder models, the authors demonstrate the potential of NLP to uncover implicit patterns in judicial documents and judgments. The predictive model for SCOTUS appeals is particularly noteworthy, as it provides a data-driven approach to forecasting legal outcomes. However, the study's limitations, such as the lack of benchmarks and the potential for generalizability issues, should be addressed in future research. The findings have important implications for both practical legal analysis and policy-making, highlighting the need for further exploration of NLP in the legal domain.

Recommendations

  • ✓ Future research should aim to establish benchmarks for predicting SCOTUS appeals to validate the performance of NLP models.
  • ✓ Expanding the scope of the study to include diverse jurisdictions and legal systems can enhance the generalizability of the findings.

Sources

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