Instance-Based Learning Algorithms
Machine Learning
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Selective sampling for example-based word sense disambiguation
Computational Linguistics
Syntactic features and word similarity for supervised metonymy resolution
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Sample Selection for Statistical Parsing
Computational Linguistics
Metonymy resolution as a classification task
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
On metonymy recognition for geographic information retrieval
International Journal of Geographical Information Science
SemEval-2007 task 08: metonymy resolution at SemEval-2007
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
A computational model of logical metonymy
ACM Transactions on Speech and Language Processing (TSLP) - Special issue on multiword expressions: From theory to practice and use, part 2
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Metonymy recognition is generally approached with complex algorithms that rely heavily on the manual annotation of training and test data. This paper will relieve this complexity in two ways. First, it will show that the results of the current learning algorithms can be replicated by the 'lazy' algorithm of Memory-Based Learning. This approach simply stores all training instances to its memory and classifies a test instance by comparing it to all training examples. Second, this paper will argue that the number of labelled training examples that is currently used in the literature can be reduced drastically. This finding can help relieve the knowledge acquisition bottleneck in metonymy recognition, and allow the algorithms to be applied on a wider scale.