Introduction to the special issue on evaluating word sense disambiguation systems
Natural Language Engineering
Evaluating sense disambiguation across diverse parameter spaces
Natural Language Engineering
Parameter optimization for machine-learning of word sense disambiguation
Natural Language Engineering
Word sense disambiguation with pattern learning and automatic feature selection
Natural Language Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
mySENSEVAL: explaining WSD system performance using target word features
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
Combining heterogeneous classifiers for word-sense disambiguation
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
The University of Alicante word sense disambiguation system
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Case-Sensitivity of Classifiers for WSD: Complex Systems Disambiguate Tough Words Better
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
Defining classifier regions for WSD ensembles using word space features
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
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In Senseval workshops for evaluating WSD systems [1,4,9], no one system or system type (classifier algorithm, type of system ensemble, extracted feature set, lexical knowledge source etc.) has been discovered that resolves all ambiguous words into their senses in a superior way. This paper presents a novel method for selecting the best system for target word based on readily available word features (number of senses, average amount of training per sense, dominant sense ratio). Applied to Senseval-3 and Senseval-2 English lexical sample state-of-art systems, a net gain of approximately 2.5 – 5.0% (respectively) in average precision per word over the best base system is achieved. The method can be applied to any base system or target word in any language.