Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Using a semantic concordance for sense identification
HLT '94 Proceedings of the workshop on Human Language Technology
An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Boosting statistical word alignment using labeled and unlabeled data
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Semi-supervised learning integrated with classifier combination for word sense disambiguation
Computer Speech and Language
Semi-supervised Word Sense Disambiguation Using the Web as Corpus
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
On the use of automatically acquired examples for all-nouns word sense disambiguation
Journal of Artificial Intelligence Research
A Reexamination of MRD-Based Word Sense Disambiguation
ACM Transactions on Asian Language Information Processing (TALIP)
Chinese chunking with tri-training learning
ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
Investigating problems of semi-supervised learning for word sense disambiguation
ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
Word Sense Disambiguation by Combining Labeled Data Expansion and Semi-Supervised Learning Method
ACM Transactions on Asian Language Information Processing (TALIP)
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Current word sense disambiguation (WSD) systems based on supervised learning are still limited in that they do not work well for all words in a language. One of the main reasons is the lack of sufficient training data. In this paper, we investigate the use of unlabeled training data for WSD, in the framework of semi-supervised learning. Four semisupervised leaming algorithms are evaluated on 29 nouns of Senseval-2 (SE2) English lexical sample task and SE2 English all-words task. Empirical results show that unlabeled data can bring significant improvement in WSD accuracy.