Abstract
The Amount of legal information that is being produced on a daily basis in courts is increasing enormously. The processing of such data has been receiving considerate attention thanks to their availability in an electronic form and the progress made in Artificial Intelligence application. Indeed, deep learning has shown promising results when used in the field of natural language processing (NLP). Neural Networks such as convolutional neural networks and recurrent neural network have been used for different NLP tasks like information retrieval, sentiment analysis and document classification. In this work, we propose a Neural Network based model with a dynamic input length for French legal text classification. The proposed approach, tested over real legal cases, outperforms baseline methods.
ICIKS 2021.
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Acknowledgments
This paper has been done under the contract PREMATTAJ 2017–2019 of the Occitanie region which is greatly acknowledged. The decisions used in this paper have been annotated by Professor Guillaume Zambrano of the University of Nîmes.
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Hammami, E., Faiz, R., Akermi, I. (2021). A Dynamic Convolutional Neural Network Approach for Legal Text Classification. In: Saad, I., Rosenthal-Sabroux, C., Gargouri, F., Arduin, PE. (eds) Information and Knowledge Systems. Digital Technologies, Artificial Intelligence and Decision Making. ICIKS 2021. Lecture Notes in Business Information Processing, vol 425. Springer, Cham. https://doi.org/10.1007/978-3-030-85977-0_6
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