Predicting and Analyzing Law-Making in Kenya
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
- 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)