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.
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Notes
eJRM - Information Retrieval System.
The acronym of eJRM-IRS is [BLIND].
For the Italian language a Snowball stemmer has been used and extended.
Court decisions are a priori labelled by legal experts.
To avoid confusion, the estimation of Coherence Similarity is based on non-normalized frequency of terms both for the query and the document.
Court decisions are obtained by crawling from the website http://www.ricercagiuridica.com/sentenze/. The dataset, after crawling, has been manually labeled by three legal experts.
Weka and SVMLIB libraries have been used for classification purposes. EJML (Efficient Java Matrix Library) has been adopted for dealing with large and sparse matrices and developing PCA-based feature reduction.
A random seed has been used to randomize the dataset to subsequently extract nine-folds as training and one-fold as testing.
Results about the classification have been micro-averaged.
These methods only compute the similarity between the court decisions and the disputant case description.
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This work is partially funded by the eJRM Project (ref.: PON01_01286).
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El Jelali, S., Fersini, E. & Messina, E. Legal retrieval as support to eMediation: matching disputant’s case and court decisions. Artif Intell Law 23, 1–22 (2015). https://doi.org/10.1007/s10506-015-9162-1
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DOI: https://doi.org/10.1007/s10506-015-9162-1