Co-training and Self-training for Word Sense Disambiguation Metadata
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- Main Title Co-training and Self-training for Word Sense Disambiguation
Author: Mihalcea, Rada, 1974-Creator Type: PersonalCreator Info: University of North Texas
Organizer of meeting: Association for Computational Linguistics. Special Interest Group on Natural Language Learning.Contributor Type: Organization
- Creation: 2004-05
- Content Description: This paper investigates the application of co-training and self-training to word sense disambiguation. Optimal and empirical parameter selection methods for co-training and self-training are investigated, with various degrees of error reduction. A new method that combines co-training with majority voting is introduced, with the effect of smoothing the bootstrapping learning curves, and improving the average performance.
- Physical Description: 8 p.
- Keyword: word sense disambiguations
- Keyword: optimal parameter settings
- Keyword: sense classifiers
- Keyword: bootstrapping
- Conference: Conference on Natural Language Learning (CoNLL), 2004, Boston, Massachusetts, United States
Name: UNT Scholarly WorksCode: UNTSW
Name: UNT College of EngineeringCode: UNTCOE
- Rights Access: public
- Archival Resource Key: ark:/67531/metadc30955
- Academic Department: Computer Science and Engineering