Soufiane El Jelali , Elisabetta Fersini , and Enza Messina have published Legal retrieval as support to eMediation: Matching disputant’s case and court decisions , forthcoming in Artificial Intelligence and Law .
Here is the abstract:
The perspective of online dispute resolution (ODR) is to develop an online electronic system aimed at solving out-of-court disputes. Among ODR schemes, eMediation is becoming an important tool for encouraging the positive settlement of an agreement among litigants. The main motivation underlying the adoption of eMediation is the time/cost reduction for the resolution of disputes compared to the ordinary justice system. In the context of eMediation, a fundamental requirement that an ODR system should meet relates to both litigants and mediators, i.e. to enable an informed negotiation by informing the parties about the rights and duties related to the case. In order to match this requirement, we propose an information retrieval system able to retrieve relevant court decisions with respect to the disputant case description. The proposed system combines machine learning and natural language processing techniques to better match disputant case descriptions (informal and concise) with court decisions (formal and verbose). Experimental results confirm the ability of the proposed solution to empower court decision retrieval, enabling therefore a well-informed eMediation process.
Filed under: Applications, Articles and papers, Technology developments Tagged: Artificial intelligence and law, Elisabetta Fersini, emediation, Enza Messina, Legal information retrieval, Legal machine learning, Legal natural language processing, Legal search, Machine learning and law, Natural language processing and law, Online dispute resolution, Online mediation, Soufiane El Jelali
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