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Clustering-based Automatic Construction of Legal Entity Knowledge Base from Contracts

2020-11-18 17:51:27
Fuqi Song, Éric de la Clergerie
arXiv_AI
           
Abstract

In contract analysis and contract automation, a knowledge base (KB) of legal entities is fundamental for performing tasks such as contract verification, contract generation and contract analytic. However, such a KB does not always exist nor can be produced in a short time. In this paper, we propose a clustering-based approach to automatically generate a reliable knowledge base of legal entities from given contracts without any supplemental references. The proposed method is robust to different types of errors brought by pre-processing such as Optical Character Recognition (OCR) and Named Entity Recognition (NER), as well as editing errors such as typos. We evaluate our method on a dataset that consists of 800 real contracts with various qualities from 15 clients. Compared to the collected ground-truth data, our method is able to recall 84\% of the knowledge.

Abstract (translated)

URL

https://arxiv.org/abs/2012.01942

PDF

https://arxiv.org/pdf/2012.01942.pdf

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