Paper Info
Title
Nonlinear Nearest-Neighbour Matching and Its Application in Legal Precedent Retrieval
Abstract
Case-Based Reasoning (CBR) has been widely and successfully applied in legal precedent retrieval. Traditional Nearest-Neighbour (NN) matching has shown that it is not capable of dealing with the situations that the values of weights or dimensional matching scores are extremely high or low. These extreme situations have nonlinear psychological effects on the aggregate marching scores. Generalized Nearest-Neighbour (GNN) matching improved NN matching in certain situations, but it is not generally applicable and it can cause an unexpected ranking. In order to improve the limitation of NN matching and complement the deficiency of GNN matching, we propose a novel Nonlinear Nearest-Neighbour (NNN) matching function based on the adjustments for nonlinear effects and the fuzzy logic inference. In this paper, we also describe how we apply NNN matching in our legal precedent retrieval system.
Year
DOI
Venue
2005
10.1109/ICITA.2005.191
ICITA (1)
Keywords
Field
DocType
nonlinear psychological effect,gnn matching,nn matching,traditional nearest-neighbour,generalized nearest-neighbour,nonlinear effect,nonlinear nearest-neighbour matching,legal precedent retrieval system,legal precedent retrieval,dimensional matching score,novel nonlinear nearest-neighbour,case base reasoning,law,fuzzy logic,case based reasoning,psychology,neural networks,information technology,information retrieval
Nearest neighbour,Nonlinear system,Ranking,Fuzzy logic,Artificial intelligence,Artificial neural network,Case-based reasoning,Legal precedent,Fuzzy logic inference
Conference
ISBN
Citations 
PageRank 
0-7695-2316-1
2
0.43
References 
Authors
10
2
Name
Order
Citations
PageRank
Ruili Wang144650.35
Yiming Zeng2252.61