Abstract
Artificial Intelligence techniques are already popular and important in the legal domain. We extract legal indicators from judicial judgments to decrease the asymmetry of information of the legal system and the access-to-justice gap. We use NLP methods to extract interesting entities/data from judgments to construct networks of lawyers and judgments. We propose metrics to rank lawyers based on their experience, wins/loss ratio and their importance in the network of lawyers. We also perform community detection in the network of judgments and propose metrics to represent the difficulty of cases capitalising on communities features.
| Original language | English |
|---|---|
| Pages (from-to) | 11-17 |
| Number of pages | 7 |
| Journal | CEUR Workshop Proceedings |
| Volume | 2645 |
| Publication status | Published - 1 Jan 2020 |
| Event | SAE 2020 Automotive Technical Papers, WONLYAUTO 2020 - Warrendale, United States Duration: 1 Jan 2020 → … |
Keywords
- Case-law analysis
- Graph mining
- Legal text
- Named entity recognition
- Natural language processing
- Network analysis