A maximum entropy approach to natural language processing
Computational Linguistics
Parameter optimization for machine-learning of word sense disambiguation
Natural Language Engineering
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Dutch word sense disambiguation: optimizing the localness of context
WSD '02 Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8
Dutch word sense disambiguation: data and preliminary results
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Finite state tools for natural language processing
Proceedings of the COLING-2000 Workshop on Using Toolsets and Architectures To Build NLP Systems
Multilingual word sense discrimination: a comparative cross-linguistic study
ACL '07 Proceedings of the Workshop on Balto-Slavonic Natural Language Processing: Information Extraction and Enabling Technologies
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In this paper, we present a corpus-based supervised word sense disambiguation (WSD) system for Dutch which combines statistical classification (maximum entropy) with linguistic information. Instead of building individual classifiers per ambiguous wordfomi, we introduce a lemma-based approach. The advantage of this novel method is that it clusters all inflected forms of an ambiguous word in one classifier, therefore augmenting the training material available to the algorithm. Testing the lemmabased model on the Dutch Senseval-2 test data, we achieve a significant increase in accuracy over the wordform model. Also, the WSD system based on lemmas is smaller and more robust.