DeepAI AI Chat
Log In Sign Up

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

by   Fuqi Song, et al.

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.


A Dataset of German Legal Documents for Named Entity Recognition

We describe a dataset developed for Named Entity Recognition in German f...

Investigating Strategies for Clause Recommendation

Clause recommendation is the problem of recommending a clause to a legal...

A Benchmark for Lease Contract Review

Extracting entities and other useful information from legal contracts is...

CLAUSEREC: A Clause Recommendation Framework for AI-aided Contract Authoring

Contracts are a common type of legal document that frequent in several d...

Runtime verification in Erlang by using contracts

During its lifetime, a program suffers several changes that seek to impr...

Cross-Domain Contract Element Extraction with a Bi-directional Feedback Clause-Element Relation Network

Contract element extraction (CEE) is the novel task of automatically ide...

Signature in Counterparts, a Formal Treatment

"Signature in counterparts" is a legal process that permits a contract b...