Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Understanding search engines: mathematical modeling and text retrieval
Understanding search engines: mathematical modeling and text retrieval
SemEval-2007 task 17: English lexical sample, SRL and all words
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
UBC-ALM: combining k-NN with SVD for WSD
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
UBC-ZAS: a k-NN based multiclassifier system to perform WSD in a reduced dimensional vector space
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Automatic semantic web annotation of named entities
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
| Hi-index | 0.00 |
In this paper a multiclassifier based approach is presented for a word sense disambiguation (WSD) problem. A vector representation is used for training and testing cases and the Singular Value Decomposition (SVD) technique is applied to reduce the dimension of the representation. The approach we present consists in creating a set of k-NN classifiers and combining the predictions generated in order to give a final word sense prediction for each case to be classified. The combination is done by applying a Bayesian voting scheme. The approach has been applied to a database of 100 words made available by the lexical sample WSD subtask of SemEval-2007 (task 17) organizers. Each of the words was considered an independent classification problem. A methodological parameter tuning phase was applied in order to optimize parameter setting for each word. Results achieved are among the best and make the approach encouraging to apply to other WSD tasks.