Towards Supporting Legal Argumentation with NLP: Is More Data Really All You Need?
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912072997011456 |
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| author | Santosh, T. Y. S. S Ashley, Kevin D. Atkinson, Katie Grabmair, Matthias |
| author_facet | Santosh, T. Y. S. S Ashley, Kevin D. Atkinson, Katie Grabmair, Matthias |
| contents | Modeling legal reasoning and argumentation justifying decisions in cases has always been central to AI & Law, yet contemporary developments in legal NLP have increasingly focused on statistically classifying legal conclusions from text. While conceptually simpler, these approaches often fall short in providing usable justifications connecting to appropriate legal concepts. This paper reviews both traditional symbolic works in AI & Law and recent advances in legal NLP, and distills possibilities of integrating expert-informed knowledge to strike a balance between scalability and explanation in symbolic vs. data-driven approaches. We identify open challenges and discuss the potential of modern NLP models and methods that integrate |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_10974 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Supporting Legal Argumentation with NLP: Is More Data Really All You Need? Santosh, T. Y. S. S Ashley, Kevin D. Atkinson, Katie Grabmair, Matthias Computation and Language Artificial Intelligence Modeling legal reasoning and argumentation justifying decisions in cases has always been central to AI & Law, yet contemporary developments in legal NLP have increasingly focused on statistically classifying legal conclusions from text. While conceptually simpler, these approaches often fall short in providing usable justifications connecting to appropriate legal concepts. This paper reviews both traditional symbolic works in AI & Law and recent advances in legal NLP, and distills possibilities of integrating expert-informed knowledge to strike a balance between scalability and explanation in symbolic vs. data-driven approaches. We identify open challenges and discuss the potential of modern NLP models and methods that integrate |
| title | Towards Supporting Legal Argumentation with NLP: Is More Data Really All You Need? |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2406.10974 |