2020/10/20 by Spyretta Leivaditi, Leivaditi, Spyretta, Julien Rossi +3 · 3 citations
Computer Science · Economics, Econometrics and Finance · Social Sciences · #Artificial Intelligence in Law #Computation and Language (cs.CL) #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Information Retrieval (cs.IR) #Law, Economics, and Judicial Systems #cs.CL #cs.IR
paper · pdf · doi:10.48550/arxiv.2010.10386
arxiv created 2020/10/20 · openalex publication_date 2020/10/20 · arxiv updated 2020/10/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Extracting entities and other useful information from legal contracts is an important task whose automation can help legal professionals perform contract reviews more efficiently and reduce relevant risks. In this paper, we tackle the problem of detecting two different types of elements that play an important role in a contract review, namely entities and red flags. The latter are terms or sentences that indicate that there is some danger or other potentially problematic situation for one or more of the signing parties. We focus on supporting the review of lease agreements, a contract type that has received little attention in the legal information extraction literature, and we define the types of entities and red flags needed for that task. We release a new benchmark dataset of 179 lease agreement documents that we have manually annotated with the entities and red flags they contain, and which can be used to train and test relevant extraction algorithms. Finally, we release a new language model, called ALeaseBERT, pre-trained on this dataset and fine-tuned for the detection of the aforementioned elements, providing a baseline for further research