@inproceedings{amit-etal-2023-convolutional,
title = "Convolutional Neural Networks can achieve binary bail judgement classification",
author = "Barman, Amit and
Roy, Devangan and
Paul, Debapriya and
Dutta, Indranil and
Guha, Shouvik Kumar and
Karmakar, Samir and
Naskar, Sudip Kumar",
editor = "D. Pawar, Jyoti and
Lalitha Devi, Sobha",
booktitle = "Proceedings of the 20th International Conference on Natural Language Processing (ICON)",
month = dec,
year = "2023",
address = "Goa University, Goa, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2023.icon-1.79/",
pages = "773--778",
abstract = "There is an evident lack of implementation of Machine Learning (ML) in the legal domain in India, and any research that does take place in this domain is usually based on data from the higher courts of law and works with English data. The lower courts and data from the different regional languages of India are often overlooked. In this paper, we deploy a Convolutional Neural Network (CNN) architecture on a corpus of Hindi legal documents. We perform a bail Prediction task with the help of a CNN model and achieve an overall accuracy of 93{\%} which is an improvement on the benchmark accuracy, set by Kapoor et al. (2022), albeit in data from 20 districts of the Indian state of Uttar Pradesh."
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<abstract>There is an evident lack of implementation of Machine Learning (ML) in the legal domain in India, and any research that does take place in this domain is usually based on data from the higher courts of law and works with English data. The lower courts and data from the different regional languages of India are often overlooked. In this paper, we deploy a Convolutional Neural Network (CNN) architecture on a corpus of Hindi legal documents. We perform a bail Prediction task with the help of a CNN model and achieve an overall accuracy of 93% which is an improvement on the benchmark accuracy, set by Kapoor et al. (2022), albeit in data from 20 districts of the Indian state of Uttar Pradesh.</abstract>
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%0 Conference Proceedings
%T Convolutional Neural Networks can achieve binary bail judgement classification
%A Barman, Amit
%A Roy, Devangan
%A Paul, Debapriya
%A Dutta, Indranil
%A Guha, Shouvik Kumar
%A Karmakar, Samir
%A Naskar, Sudip Kumar
%Y D. Pawar, Jyoti
%Y Lalitha Devi, Sobha
%S Proceedings of the 20th International Conference on Natural Language Processing (ICON)
%D 2023
%8 December
%I NLP Association of India (NLPAI)
%C Goa University, Goa, India
%F amit-etal-2023-convolutional
%X There is an evident lack of implementation of Machine Learning (ML) in the legal domain in India, and any research that does take place in this domain is usually based on data from the higher courts of law and works with English data. The lower courts and data from the different regional languages of India are often overlooked. In this paper, we deploy a Convolutional Neural Network (CNN) architecture on a corpus of Hindi legal documents. We perform a bail Prediction task with the help of a CNN model and achieve an overall accuracy of 93% which is an improvement on the benchmark accuracy, set by Kapoor et al. (2022), albeit in data from 20 districts of the Indian state of Uttar Pradesh.
%U https://aclanthology.org/2023.icon-1.79/
%P 773-778
Markdown (Informal)
[Convolutional Neural Networks can achieve binary bail judgement classification](https://aclanthology.org/2023.icon-1.79/) (Barman et al., ICON 2023)
ACL
- Amit Barman, Devangan Roy, Debapriya Paul, Indranil Dutta, Shouvik Kumar Guha, Samir Karmakar, and Sudip Kumar Naskar. 2023. Convolutional Neural Networks can achieve binary bail judgement classification. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 773–778, Goa University, Goa, India. NLP Association of India (NLPAI).