The nature of statistical learning theory
The nature of statistical learning theory
A maximum entropy approach to natural language processing
Computational Linguistics
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
A maximum entropy-based word sense disambiguation system
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Adaptive language modeling using the maximum entropy principle
HLT '93 Proceedings of the workshop on Human Language Technology
Improving POS tagging for ungrammatical phrases
Proceedings of the 2012 Joint International Conference on Human-Centered Computer Environments
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In this paper we discuss to what extent the choice of one particular Part-of-Speech (PoS) tagger determines the results obtained by a word sense disambiguation (WSD) system We have chosen several PoS taggers and two WSD methods By combining them, and using different kind of information, several experiments have been carried out The WSD systems have been evaluated using the corpora of the lexical sample task of senseval-3 for English The results show that some PoS taggers work better with one specific method That is, selecting the right combination of these tools, could improve the results obtained by a WSD system.