Discovering Significant Topics from Legal Decisions with Selective
  Inference
Jerrold Soh
We propose and evaluate an automated pipeline for discovering significant
topics from legal decision texts by passing features synthesized with topic
models through penalised regressions and post-selection significance tests. The
method identifies case topics significantly correlated with outcomes,
topic-word distributions which can be manually-interpreted to gain insights
about significant topics, and case-topic weights which can be used to identify
representative cases for each topic. We demonstrate the method on a new dataset
of domain name disputes and a canonical dataset of European Court of Human
Rights violation cases. Topic models based on latent semantic analysis as well
as language model embeddings are evaluated. We show that topics derived by the
pipeline are consistent with legal doctrines in both areas and can be useful in
other related legal analysis tasks.