Alignment of Custom Standards by Machine Learning Algorithms

Clicks: 393
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.
Reference Key
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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