Some advances in transformation-based part of speech tagging
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Using corpus statistics and WordNet relations for sense identification
Computational Linguistics - Special issue on word sense disambiguation
Evaluating sense disambiguation across diverse parameter spaces
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)
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This paper describes our word sense disambiguation (WSD) system participating in the SemEval-2007 tasks. The core system is a fully supervised system based on a Naïve Bayes classifier using multiple knowledge sources. Toward a larger goal of incorporating the intrinsic nature of individual target words in disambiguation, thus introducing a cognitive element in automatic WSD, we tried to fine-tune the results obtained from the core system with human-informed feature preference, and compared it with automatic feature selection as commonly practised in statistical WSD. Despite the insignificant improvement observed in this preliminary attempt, more systematic analysis remains to be done for a cognitively plausible account of the factors underlying the lexical sensitivity of WSD, which would inform and enhance the development of WSD systems in return.