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.

EL JELALI, S., Fersini, E., Messina, V. (2015). Legal retrieval as support to eMediation: matching disputant’s case and court decisions. ARTIFICIAL INTELLIGENCE AND LAW, 23(1), 1-22 [10.1007/s10506-015-9162-1].

Legal retrieval as support to eMediation: matching disputant’s case and court decisions

EL JELALI, SOUFIANE
Primo
;
FERSINI, ELISABETTA
;
MESSINA, VINCENZINA
2015

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.
Articolo in rivista - Articolo scientifico
eMediation; Information retrieval; Machine learning; Natural language processing; Artificial Intelligence; Law
English
2015
23
1
1
22
none
EL JELALI, S., Fersini, E., Messina, V. (2015). Legal retrieval as support to eMediation: matching disputant’s case and court decisions. ARTIFICIAL INTELLIGENCE AND LAW, 23(1), 1-22 [10.1007/s10506-015-9162-1].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/135761
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