Abstract
The quantity of legal information that is being produced on a daily basis in courts is growing enormously. The processing of such data has been gaining considerable attention thanks to their availability in an electronic form and the advancement made in Artificial Intelligence applications. Indeed, deep learning has offered promising results when used in the field of natural language processing (NLP). Neural Networks such as recurrent neural network and convolutional neural networks have been used for different NLP tasks like information retrieval, document classification and sentiment analysis. In this paper, we present a Neural Network based model with a dynamic input length for classifying legal opinions from cases seen by the Supreme Court of the United States (SCOTUS) (https://www.kaggle.com/gqfiddler/scotus-opinions). The proposed model, tested over a real-world legal opinions dataset, by the way, proved better performance than the other baseline methods.
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This work is supported by LARODEC laboratory of the Higher Institute Management, University of Tunis.
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Hammami, E., Faiz, R., Slama, S.B. (2022). A Study of Dynamic Convolutional Neural Network Technique for SCOTUS Legal Opinions Data Classification. In: Lossio-Ventura, J.A., et al. Information Management and Big Data. SIMBig 2021. Communications in Computer and Information Science, vol 1577. Springer, Cham. https://doi.org/10.1007/978-3-031-04447-2_6
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