@inproceedings{babafemi-akinfaderin-2020-predicting,
title = "Predicting and Analyzing Law-Making in {K}enya",
author = "Babafemi, Oyinlola and
Akinfaderin, Adewale",
booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
month = jul,
year = "2020",
address = "Seattle, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.winlp-1.26",
doi = "10.18653/v1/2020.winlp-1.26",
pages = "103--106",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Predicting and Analyzing Law-Making in Kenya
%A Babafemi, Oyinlola
%A Akinfaderin, Adewale
%S Proceedings of the The Fourth Widening Natural Language Processing Workshop
%D 2020
%8 jul
%I Association for Computational Linguistics
%C Seattle, USA
%F babafemi-akinfaderin-2020-predicting
%X 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.
%R 10.18653/v1/2020.winlp-1.26
%U https://aclanthology.org/2020.winlp-1.26
%U https://doi.org/10.18653/v1/2020.winlp-1.26
%P 103-106
Markdown (Informal)
[Predicting and Analyzing Law-Making in Kenya](https://aclanthology.org/2020.winlp-1.26) (Babafemi & Akinfaderin, WiNLP 2020)
ACL
- Oyinlola Babafemi and Adewale Akinfaderin. 2020. Predicting and Analyzing Law-Making in Kenya. In Proceedings of the The Fourth Widening Natural Language Processing Workshop, pages 103–106, Seattle, USA. Association for Computational Linguistics.