Predicting and Analyzing Law-Making in Kenya

Oyinlola Babafemi, Adewale Akinfaderin


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.
Anthology ID:
2020.winlp-1.26
Volume:
Proceedings of the The Fourth Widening Natural Language Processing Workshop
Month:
July
Year:
2020
Address:
Seattle, USA
Venues:
ACL | WS | WiNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
103–106
Language:
URL:
https://www.aclweb.org/anthology/2020.winlp-1.26
DOI:
10.18653/v1/2020.winlp-1.26
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Video:
 http://slideslive.com/38929565