Artificial Intelligence in Law

Lecture Notes in Computer Science, 2010

Proceedings of the V …, 2007

Empirical Software Engineering, 2021

Cornell University - arXiv, 2022

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023

Text preprocessing is the most essential and foremost step for any Machine Learning model. The raw data needs to be cleaned and pre-processed to get better performance. It is the method to clean the data and makes it ready to feed the data to the model. Text classification is the heart of many software systems that involve text documents processing. The purpose of text classification is to classify the text documents automatically into two or many defined categories. In this paper ,various preprocessing and classification approaches are used such as NLP, Machine Learning, etc from patent documents.

Grail of Science

This paper considers the smart contracts development process based on business rules using natural language processing as the research object. The research subject includes software components for creating smart contracts based on business rules using natural language processing. The research aims to simplify the software component development for decentralized systems by using smart contracts generation from business rules written in natural language. This study considers smart contract development approaches and technologies, intelligent text processing methods, as well as software development techniques using the Python programming language for the experimental implementation of the proposed solution. This study outlines the relevance of this research, provides a state-of-the-art analysis, proposes the improved procedure of smart contracts’ development and deployment, and suggests an algorithm for smart contract generation based on business rules.

Legal Convention 2024, 2024

The legal profession is undergoing a paradigm shift driven by the integration of artificial intelligence (AI), with Natural Language Processing (NLP) emerging as one of the most transformative technologies in contract review and due diligence. In an era where the volume and complexity of contracts in business transactions continue to rise exponentially, traditional manual review processes are rapidly becoming inefficient, expensive, and prone to human error. As a result, law firms and legal departments are seeking innovative solutions that not only streamline workflows but also enhance accuracy and scalability. This research explores how NLP technology can dramatically reduce the time and resources required for contract review and due diligence while improving accuracy and consistency. We analyze the current state of NLP in legal applications, examining innovations such as machine learning algorithms for contract classification, entity extraction for key clause identification, and semantic analysis for understanding contract language (Surden, 2019a). However, the integration of NLP into legal practice is not without challenges. We address critical concerns including data privacy, maintenance of attorney-client privilege, and the need for human oversight in legal decision-making (Chagal-Feferkorn, 2019). The paper also discusses the implications for legal education and practice, as successful implementation of NLP systems necessitates new skills and approaches from legal professionals (McGinnis & Pearce, 2014)

2012

In the paper, problems of legal information digitalization are investigated. Conditions for extraction information from legal texts related to the common ones processing (non-legal terms) are outlined. Sample results of similarity analysis are presented. Further research aimed at semantic analysis of legal texts are outlined.

Informatics

The aim of the research is to semi-automate the process of generating formal specifications from legal contracts in natural language text form. Towards this end, the paper presents a tool, named ContrattoA, that semi-automatically conducts semantic annotation of legal contract text using an ontology for legal contracts. ContrattoA was developed through two iterations where lexical patterns were defined for legal concepts and their effectiveness was evaluated with experiments. The first iteration was based on a handful of sample contracts and resulted in defining lexical patterns for recognizing concepts in the ontology; these were evaluated with an empirical study where one group of subjects was asked to annotate legal text manually, while a second group edited the annotations generated by ContrattoA. The second iteration focused on the lexical patterns for the core contract concepts of obligation and power where results of the first iteration were mixed. On the basis of an extended...