Nonlinear nearest-neighbour matching and its application in legal precedent retrieval

Ruili Wang, Yiming Zeng

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - 3rd International Conference on Information Technology and Applications, ICITA 2005
Pages341-346
Number of pages6
Publication statusPublished - 2005
Externally publishedYes
Event3rd International Conference on Information Technology and Applications, ICITA 2005 - Sydney, Australia
Duration: 4 Jul 20057 Jul 2005

Publication series

NameProceedings - 3rd International Conference on Information Technology and Applications, ICITA 2005
VolumeI

Conference

Conference3rd International Conference on Information Technology and Applications, ICITA 2005
Country/TerritoryAustralia
CitySydney
Period4/07/057/07/05

ASJC Scopus subject areas

  • General Engineering

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