# Discovering Significant Topics from Legal Decisions with Selective Inference

arXiv: https://arxiv.org/abs/2401.01068 · HTML: https://mlsanity.com/paper/2401.01068/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 82 references that are in the map.
- First posted on arXiv 2 Jan 2024.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://mlsanity.com/paper/2110.00976/) (2021)
- [Neural Legal Judgment Prediction in English](https://mlsanity.com/paper/1906.02059/) (2019)
- [Exact post-selection inference, with application to the lasso](https://mlsanity.com/paper/1311.6238/) (2013)
- [Pre-trained Language Models for the Legal Domain: A Case Study on Indian Law](https://mlsanity.com/paper/2209.06049/) (2022)
- [Predicting the Law Area and Decisions of French Supreme Court Cases](https://mlsanity.com/paper/1708.01681/) (2017)
- [A network approach to topic models](https://mlsanity.com/paper/1708.01677/) (2017)
- [How Many Topics? Stability Analysis for Topic Models](https://mlsanity.com/paper/1404.4606/) (2014)
- [Legal Area Classification: A Comparative Study of Text Classifiers on Singapore Supreme Court Judgments](https://mlsanity.com/paper/1904.06470/) (2019)
- [Experiments on Generalizability of BERTopic on Multi-Domain Short Text](https://mlsanity.com/paper/2212.08459/) (2022)
- [Post-selection inference for L1-penalized likelihood models](https://mlsanity.com/paper/1602.07358/) (2016)

## Built on by (most cited first, 1 of 1)

- [Topic Classification of Case Law Using a Large Language Model and a New Taxonomy for UK Law: AI Insights into Summary Judgment](https://mlsanity.com/paper/2405.12910/) (2024)

## Abstract (arXiv)

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.