@inproceedings{097eb7e94e684c7ca0c942bb1293f2d0,
title = "Defining classifier regions for WSD ensembles using word space features",
abstract = "Based on recent evaluation of word sense disambiguation (WSD) systems [10], disambiguation methods have reached a standstill. In [10] we showed that it is possible to predict the best system for target word using word features and that using this 'optimal ensembling method' more accurate WSD ensembles can be built (3-5% over Senseval state of the art systems with the same amount of possible potential remaining). In the interest of developing if more accurate ensembles, we here define the strong regions for three popular and effective classifiers used for WSD task (Naive Bayes - NB, Support Vector Machine - SVM, Decision Rules - D) using word features (word grain, amount of positive and negative training examples, dominant sense ratio). We also discuss the effect of remaining factors (feature-based).",
author = "Saarikoski, {Harri M.T.} and Steve Legrand and Alexander Gelbukh",
year = "2006",
doi = "10.1007/11925231_82",
language = "Ingl{\'e}s",
isbn = "3540490264",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "855--867",
booktitle = "MICAI 2006",
address = "Alemania",
note = "5th Mexican International Conference on Artificial Intelligence, MICAI 2006: Advances in Artificial Intelligence ; Conference date: 13-11-2006 Through 17-11-2006",
}