Legal reasoning by analogy with neural networks: the representation problem

This paper explores how neural networks can be used for legal reasoning, specifically focusing on reasoning by analogy. It addresses the challenge of representing legal cases and precedents in a way that a neural network can effectively process to find similarities and differences, ultimately aiding in making legal arguments or predictions. The research highlights the importance of feature selection and representation in enabling neural networks to perform legal reasoning tasks.