Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-20087
Publication type: Conference paper
Type of review: Peer review (publication)
Title: LEDGAR : a large-scale multi-label corpus for text classification of legal provisions in contracts
Authors : Tuggener, Don
von Däniken, Pius
Peetz, Thomas
Cieliebak, Mark
et. al : No
DOI : 10.21256/zhaw-20087
Proceedings: Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)
Pages : 1228
Pages to: 1234
Conference details: 12th Language Resources and Evaluation Conference (LREC) 2020
Issue Date: May-2020
Publisher / Ed. Institution : European Language Resources Association
Language : English
Subjects : Multilabel text classification; Legal nlp; Natural language processing
Subject (DDC) : 005: Computer programming, programs and data
340: Law
Abstract: We present LEDGAR, a multilabel corpus of legal provisions in contracts. The corpus was crawled and scraped from the public domain (SEC filings) and is, to the best of our knowledge, the first freely available corpus of its kind. Since the corpus was constructed semi-automatically, we apply and discuss various approaches to noise removal. Due to the rather large labelset of over 12'000 labels annotated in almost 100'000 provisions in over 60'000 contracts, we believe the corpus to be of interest for research in the field of Legal NLP, (large-scale or extreme) text classification, as well as for legal studies. We discuss several methods to sample subcopora from the corpus and implement and evaluate different automatic classification approaches. Finally, we perform transfer experiments to evaluate how well the classifiers perform on contracts stemming from outside the corpus.
URI: https://www.aclweb.org/anthology/2020.lrec-1.154
https://digitalcollection.zhaw.ch/handle/11475/20087
Fulltext version : Published version
License (according to publishing contract) : CC BY-NC 4.0: Attribution - Non commercial 4.0 International
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Published as part of the ZHAW project : SCAI: Smart Contract Analytics using Artificial Intelligence
Appears in Collections:Publikationen School of Engineering

Files in This Item:
File Description SizeFormat 
2020_Tuggener-etal_LEDGAR_LREC.pdf218.08 kBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.