Alignment of Custom Standards by Machine Learning Algorithms
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 SV...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Babes-Bolyai University, Cluj-Napoca
2010-09-01
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| Series: | Studia Universitatis Babes-Bolyai: Series Informatica |
| Online Access: | http://www.cs.ubbcluj.ro/apps/reviste/index.php/studia-i/article/view/14 |
| Summary: | 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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| ISSN: | 1224-869X |