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 Fourth Widening Natural Language Processing Workshop
Month:
July
Year:
2020
Address:
Seattle, USA
Editors:
Rossana Cunha, Samira Shaikh, Erika Varis, Ryan Georgi, Alicia Tsai, Antonios Anastasopoulos, Khyathi Raghavi Chandu
Venue:
WiNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
103–106
Language:
URL:
https://aclanthology.org/2020.winlp-1.26
DOI:
10.18653/v1/2020.winlp-1.26
Bibkey:
Cite (ACL):
Oyinlola Babafemi and Adewale Akinfaderin. 2020. Predicting and Analyzing Law-Making in Kenya. In Proceedings of the Fourth Widening Natural Language Processing Workshop, pages 103–106, Seattle, USA. Association for Computational Linguistics.
Cite (Informal):
Predicting and Analyzing Law-Making in Kenya (Babafemi & Akinfaderin, WiNLP 2020)
Copy Citation:
Video:
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