Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Sample Selection for Statistical Parsing
Computational Linguistics
Finding predominant word senses in untagged text
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
An empirical study of the behavior of active learning for word sense disambiguation
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Unknown word sense detection as outlier detection
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Novel semantic features for verb sense disambiguation
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Good seed makes a good crop: accelerating active learning using language modeling
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
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An annotation project typically has an abundant supply of unlabeled data that can be drawn from some corpus, but because the labeling process is expensive, it is helpful to pre-screen the pool of the candidate instances based on some criterion of future usefulness. In many cases, that criterion is to improve the presence of the rare classes in the data to be annotated. We propose a novel method for solving this problem and show that it compares favorably to a random sampling baseline and a clustering algorithm.