Frank Giaoui
ABSTRACT
Motivated by the subjective decision making and lack of strict protocols in damages as a remedy for contract breach, this project uses natural legal language processing (NLLP) and artificial intelligence (AI) techniques to analyze patterns in contract law cases and reduce uncertainty in their outcome.
A 'hybrid' model combining heuristics, NLLP & the results of an LSTM based model into an XGBoost regressor along with contextual information had the best performance for the classification of entity types from unstructured proceedings text. Linear regressors were developed to approximate the Recovery Rate and the Win Rate using a set of 6 engineered features likely to affect the outcome.
- Frank Giaoui. 2019. Indemnisation du Préjudice Economique. Harmattan, Paris, France. 724 pages. https://isbnsearch.org/isbn/9782343180670Google Scholar
- Frank Giaoui. 2019. Une évaluation innovante des dommages et intérêts pour traduire les faits en règles de droit et réduire l'imprévisibilité judiciaire. In La Revue des Contrats. Vol. 1. Lextenso, Paris, France, 164--181. https://www.labase-lextenso.fr/revue-des-contrats/RDC115y3Google Scholar
- Frank Giaoui. 2020. Towards Legally Reviewable Damage Awards. Corporate and Business Law Journal 173 (2020), 173--229. http://cablj.org/wp-content/uploads/2020/02/W2020-FINAL-F_Giaoui-.pdfGoogle Scholar
- Frank Giaoui. 2022. Breaches of Agreements to Negotiate: A Comparative Analysis of Damages. American Journal of Trade and Policy 9, 2 (2022), 77--98. https://doi.org/10.18034/ajtp.v9i2.623Google ScholarCross Ref
Index Terms
- Applying NLLP and ML to Predict Damages as a Remedy for Contract Breach
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