Text Analytics Can Predict Contract Fairness, Transparency and Applicability

Nicola Assolini, Adelaide Baronchelli, Matteo Cristani, Luca Pasetto, Francesco Olivieri, Roberto Ricciuti, Claudio Tomazzoli

2021

Abstract

There is a growing attention, in the research communities of political economics, onto the potential of text analytics in classifying documents with economic content. This interest extends the data analytics approach that has been the traditional base for economic theory with scientific perspective. To devise a general method for prediction applicability, we identify some phases of a methodology and perform tests on a large well-structured repository of resource contracts containing documents related to resources. The majority of these contracts involve mining resources. In this paper we prove that, by the usage of text analytics measures, we can cluster these documents on three indicators: fairness of the contract content, transparency of the document themselves, and applicability of the clauses of the contract intended to guarantee execution on an international basis. We achieve these results, consistent with a gold-standard test obtained with human experts, using text similarity based on the basic notions of bag of words, the index tf-idf, and three distinct cut-off measures.

Paper Citation

in Harvard Style

Assolini N., Baronchelli A., Cristani M., Pasetto L., Olivieri F., Ricciuti R. and Tomazzoli C. (2021). Text Analytics Can Predict Contract Fairness, Transparency and Applicability. In Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-536-4, pages 316-323. DOI: 10.5220/0010660700003058

in Bibtex Style

@conference{webist21,
author={Nicola Assolini and Adelaide Baronchelli and Matteo Cristani and Luca Pasetto and Francesco Olivieri and Roberto Ricciuti and Claudio Tomazzoli},
title={Text Analytics Can Predict Contract Fairness, Transparency and Applicability},
booktitle={Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2021},
pages={316-323},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010660700003058},
isbn={978-989-758-536-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Text Analytics Can Predict Contract Fairness, Transparency and Applicability
SN - 978-989-758-536-4
AU - Assolini N.
AU - Baronchelli A.
AU - Cristani M.
AU - Pasetto L.
AU - Olivieri F.
AU - Ricciuti R.
AU - Tomazzoli C.
PY - 2021
SP - 316
EP - 323
DO - 10.5220/0010660700003058