Alignment of Custom Standards by Machine Learning Algorithms

Clicks: 302
ID: 74986
2010
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Abstract
Building an efficient model for automatic alignment of terminologies would bring a significant improvement to the information retrieval process. We have developed and compared two machine learning based algorithms whose aim is to align 2 custom standards built on a 3 level taxonomy, using kNN and SVM classifiers that work on a vector representation consisting of several similarity measures. The weights utilized by the kNN were optimized with an evolutionary algorithm, while the SVM classifier's hyper-parameters were optimized with a grid search algorithm. The database used for train was semi automatically obtained by using the Coma++ tool. The performance of our aligners is shown by the results obtained on the test set.
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sirbu2010alignmentstudia Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sirbu, Adela;Diosan, Laura;Rogozan, Alexandrina;Pecuchet, Jean-Pierre;
Journal studia universitatis babes-bolyai: series informatica
Year 2010
DOI
DOI not found
URL
http://www.cs.ubbcluj.ro/apps/reviste/index.php/studia-i/article/view/14
Keywords
Electrical engineering. Electronics. Nuclear engineering psychology computer engineering. computer hardware electronic computers. computer science mathematical geography. cartography

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