Towards A Structured Overview of Use Cases for Natural Language Processing in the Legal Domain: A German Perspective
- URL: http://arxiv.org/abs/2404.18759v2
- Date: Thu, 2 May 2024 09:56:51 GMT
- Title: Towards A Structured Overview of Use Cases for Natural Language Processing in the Legal Domain: A German Perspective
- Authors: Juraj Vladika, Stephen Meisenbacher, Martina Preis, Alexandra Klymenko, Florian Matthes,
- Abstract summary: In recent years, the field of Legal Tech has risen in prevalence, as the Natural Language Processing (NLP) and legal disciplines have combined forces to digitalize legal processes.
In this work, we aim to build a structured overview of Legal Tech use cases, grounded in NLP literature, but also supplemented by voices from legal practice in Germany.
- Score: 43.662441393491584
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In recent years, the field of Legal Tech has risen in prevalence, as the Natural Language Processing (NLP) and legal disciplines have combined forces to digitalize legal processes. Amidst the steady flow of research solutions stemming from the NLP domain, the study of use cases has fallen behind, leading to a number of innovative technical methods without a place in practice. In this work, we aim to build a structured overview of Legal Tech use cases, grounded in NLP literature, but also supplemented by voices from legal practice in Germany. Based upon a Systematic Literature Review, we identify seven categories of NLP technologies for the legal domain, which are then studied in juxtaposition to 22 legal use cases. In the investigation of these use cases, we identify 15 ethical, legal, and social aspects (ELSA), shedding light on the potential concerns of digitally transforming the legal domain.
Related papers
- Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges [4.548047308860141]
Natural Language Processing is revolutionizing the way legal professionals and laypersons operate in the legal field.
This survey follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework, reviewing 148 studies, with a final selection of 127 after manual filtering.
It explores foundational concepts related to Natural Language Processing in the legal domain.
arXiv Detail & Related papers (2024-10-25T01:17:02Z) - InternLM-Law: An Open Source Chinese Legal Large Language Model [72.2589401309848]
InternLM-Law is a specialized LLM tailored for addressing diverse legal queries related to Chinese laws.
We meticulously construct a dataset in the Chinese legal domain, encompassing over 1 million queries.
InternLM-Law achieves the highest average performance on LawBench, outperforming state-of-the-art models, including GPT-4, on 13 out of 20 subtasks.
arXiv Detail & Related papers (2024-06-21T06:19:03Z) - The Law and NLP: Bridging Disciplinary Disconnects [11.828797013800594]
We argue that the slow uptake of NLP in legal practice is exacerbated by a disconnect between the needs of the legal community and the focus of NLP researchers.
We discuss examples of legal NLP tasks that promise to bridge disciplinary disconnects and highlight interesting areas for legal NLP research that remain underexplored.
arXiv Detail & Related papers (2023-10-22T16:34:31Z) - Analysing similarities between legal court documents using natural
language processing approaches based on Transformers [0.0]
This work targets the problem of detecting the degree of similarity between judicial documents that can be achieved in the inference group.
It applies six NLP techniques based on the transformers architecture to a case study of legal proceedings in the Brazilian judicial system.
arXiv Detail & Related papers (2022-04-14T18:25:56Z) - An Uncommon Task: Participatory Design in Legal AI [64.54460979588075]
We examine a notable yet understudied AI design process in the legal domain that took place over a decade ago.
We show how an interactive simulation methodology allowed computer scientists and lawyers to become co-designers.
arXiv Detail & Related papers (2022-03-08T15:46:52Z) - Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents [56.40163943394202]
We release the Longformer-based pre-trained language model, named as Lawformer, for Chinese legal long documents understanding.
We evaluate Lawformer on a variety of LegalAI tasks, including judgment prediction, similar case retrieval, legal reading comprehension, and legal question answering.
arXiv Detail & Related papers (2021-05-09T09:39:25Z) - On the Ethical Limits of Natural Language Processing on Legal Text [9.147707153504117]
We argue that researchers struggle when it comes to identifying ethical limits to using natural language processing systems.
We place emphasis on three crucial normative parameters which have, to the best of our knowledge, been underestimated by current debates.
For each of these three parameters we provide specific recommendations for the legal NLP community.
arXiv Detail & Related papers (2021-05-06T15:22:24Z) - LEGAL-BERT: The Muppets straight out of Law School [52.53830441117363]
We explore approaches for applying BERT models to downstream legal tasks, evaluating on multiple datasets.
Our findings indicate that the previous guidelines for pre-training and fine-tuning, often blindly followed, do not always generalize well in the legal domain.
We release LEGAL-BERT, a family of BERT models intended to assist legal NLP research, computational law, and legal technology applications.
arXiv Detail & Related papers (2020-10-06T09:06:07Z) - How Does NLP Benefit Legal System: A Summary of Legal Artificial
Intelligence [81.04070052740596]
Legal Artificial Intelligence (LegalAI) focuses on applying the technology of artificial intelligence, especially natural language processing, to benefit tasks in the legal domain.
This paper introduces the history, the current state, and the future directions of research in LegalAI.
arXiv Detail & Related papers (2020-04-25T14:45:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.