# Using attention methods to predict judicial outcomes

arXiv: https://arxiv.org/abs/2207.08823 · HTML: https://mlsanity.com/paper/2207.08823/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 92 references that are in the map.
- First posted on arXiv 18 Jul 2022.

## Builds on (most cited first, 11 of 11)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence](https://mlsanity.com/paper/2004.12158/) (2020)
- [Learning to Predict Charges for Criminal Cases with Legal Basis](https://mlsanity.com/paper/1707.09168/) (2017)
- [Exploring the Use of Text Classification in the Legal Domain](https://mlsanity.com/paper/1710.09306/) (2017)
- [Predicting the Law Area and Decisions of French Supreme Court Cases](https://mlsanity.com/paper/1708.01681/) (2017)
- [Extreme Multi-Label Legal Text Classification: A case study in EU Legislation](https://mlsanity.com/paper/1905.10892/) (2019)
- [Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks](https://mlsanity.com/paper/1708.06025/) (2017)
- [Legal Question Answering using Ranking SVM and Deep Convolutional Neural Network](https://mlsanity.com/paper/1703.05320/) (2017)
- [Toward Extractive Summarization of Online Forum Discussions via Hierarchical Attention Networks](https://mlsanity.com/paper/1805.10390/) (2018)
- [Actionable and Political Text Classification using Word Embeddings and LSTM](https://mlsanity.com/paper/1607.02501/) (2016)
- [Using Artificial Intelligence to Support Compliance with the General Data Protection Regulation](https://mlsanity.com/paper/1809.05762/) (2018)

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

- [Empirical analysis of binding precedent efficiency in Brazilian Supreme Court via case classification](https://mlsanity.com/paper/2407.07004/) (2024)

## Abstract (arXiv)

Legal Judgment Prediction is one of the most acclaimed fields for the combined area of NLP, AI, and Law. By legal prediction we mean an intelligent systems capable to predict specific judicial characteristics, such as judicial outcome, a judicial class, predict an specific case. In this research, we have used AI classifiers to predict judicial outcomes in the Brazilian legal system. For this purpose, we developed a text crawler to extract data from the official Brazilian electronic legal systems. These texts formed a dataset of second-degree murder and active corruption cases. We applied different classifiers, such as Support Vector Machines and Neural Networks, to predict judicial outcomes by analyzing textual features from the dataset. Our research showed that Regression Trees, Gated Recurring Units and Hierarchical Attention Networks presented higher metrics for different subsets. As a final goal, we explored the weights of one of the algorithms, the Hierarchical Attention Networks, to find a sample of the most important words used to absolve or convict defendants.