Progress in Textual Case-Based Reasoning: Predicting the Outcome of Legal Cases from Text
Abstract
This paper reports on a project that explored reasoning with textual cases in the context of legal reasoning. The work is anchored in both Case-Based Reasoning (CBR) and AI and Law. It introduces the SMILE+IBP framework that generates a case-based analysis and prediction of the outcome of a legal case given a brief textual summary of the case facts. The focal research question in this work was to find a good text representation for text classification. An evaluation showed that replacing case-specific names by roles and adding NLP lead to higher performance for assigning CBR indices. The NLP-based representation produced the best results for reasoning with the automatically indexed cases.
Cite
Text
Brüninghaus and Ashley. "Progress in Textual Case-Based Reasoning: Predicting the Outcome of Legal Cases from Text." AAAI Conference on Artificial Intelligence, 2006.Markdown
[Brüninghaus and Ashley. "Progress in Textual Case-Based Reasoning: Predicting the Outcome of Legal Cases from Text." AAAI Conference on Artificial Intelligence, 2006.](https://mlanthology.org/aaai/2006/bruninghaus2006aaai-progress/)BibTeX
@inproceedings{bruninghaus2006aaai-progress,
title = {{Progress in Textual Case-Based Reasoning: Predicting the Outcome of Legal Cases from Text}},
author = {Brüninghaus, Stefanie and Ashley, Kevin D.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2006},
pages = {1577-1580},
url = {https://mlanthology.org/aaai/2006/bruninghaus2006aaai-progress/}
}