Summary of Natural Language Processing For the Legal Domain: a Survey Of Tasks, Datasets, Models, and Challenges, by Farid Ariai and Gianluca Demartini
Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges
by Farid Ariai, Gianluca Demartini
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This survey reviews 148 studies on Natural Language Processing (NLP) in the legal domain, selecting 127 after manual filtering. The review explores foundational concepts, illustrating unique aspects and challenges of processing legal texts. It covers NLP tasks specific to legal text, such as summarization, named entity recognition, question answering, text classification, and judgment prediction. The survey also analyzes language models developed for the legal domain and identifies 15 open research challenges, including bias in AI applications, improving model interpretability, and enhancing explainability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers can help with legal work. Right now, computers are getting better at understanding words and sentences, which could make a big difference in the law. For example, it could help lawyers find important information in long documents or answer simple questions about what a piece of legislation means. The researchers looked at lots of other studies to see how well these computer programs work for legal tasks like summarizing documents or recognizing important words. They also found some challenges that need to be solved before computers can really help with law. |
Keywords
» Artificial intelligence » Named entity recognition » Natural language processing » Nlp » Question answering » Summarization » Text classification
