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

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 knowledge base 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 produced by preprocessing 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.