Predicting and Analyzing Law-Making in Kenya
Computation and Language
2020-06-11 v1 Computers and Society
Machine Learning
Abstract
Modelling and analyzing parliamentary legislation, roll-call votes and order of proceedings in developed countries has received significant attention in recent years. In this paper, we focused on understanding the bills introduced in a developing democracy, the Kenyan bicameral parliament. We developed and trained machine learning models on a combination of features extracted from the bills to predict the outcome - if a bill will be enacted or not. We observed that the texts in a bill are not as relevant as the year and month the bill was introduced and the category the bill belongs to.
Cite
@article{arxiv.2006.05493,
title = {Predicting and Analyzing Law-Making in Kenya},
author = {Oyinlola Babafemi and Adewale Akinfaderin},
journal= {arXiv preprint arXiv:2006.05493},
year = {2020}
}
Comments
Accepted at 4th Widening NLP Workshop, Annual Meeting of the Association for Computational Linguistics, ACL 2020