The interaction of knowledge sources in word sense disambiguation
Computational Linguistics
Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
Parameter optimization for machine-learning of word sense disambiguation
Natural Language Engineering
Integrating multiple knowledge sources to disambiguate word sense: an exemplar-based approach
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Memory-based morphological analysis
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Scaling to very very large corpora for natural language disambiguation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Classifier optimization and combination in the English all words task
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
SenseLearner: word sense disambiguation for all words in unrestricted text
ACLdemo '05 Proceedings of the ACL 2005 on Interactive poster and demonstration sessions
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Combining knowledge- and corpus-based word-sense-disambiguation methods
Journal of Artificial Intelligence Research
STEP '08 Proceedings of the 2008 Conference on Semantics in Text Processing
An experimental study on unsupervised graph-based word sense disambiguation
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
Hi-index | 0.00 |
In this paper, we evaluate the results of the Antwerp University word sense disambiguation system in the English all words task of SENSEVAL-2. In this approach, specialized memory-based word-experts were trained per word-POS combination. Through optimization by cross-validation of the individual component classifiers and the voting scheme for combining them, the best possible word-expert was determined. In the competition, this word-expert architecture resulted in accuracies of 63.6% (fine-grained) and 64.5% (coarse-grained) on the SENSEVAL-2 test data.In order to better understand these results, we investigated whether classifiers trained on different information sources performed differently on the different part-of-speech categories. Furthermore, the results were evaluated in terms of the available number of training items, the number of senses, and the sense distributions in the data set. We conclude that there is no information source which is optimal over all word-experts. Selecting the optimal classifier/voter for each single word-expert, however, leads to major accuracy improvements. We furthermore show that accuracies do not so much depend on the available number of training items, but largely on polysemy and sense distributions.