Word sense disambiguation using a second language monolingual corpus
Computational Linguistics
Distributional clustering of words for text classification
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
Document clustering using word clusters via the information bottleneck method
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Iterative Double Clustering for Unsupervised and Semi-supervised Learning
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Enhanced word clustering for hierarchical text classification
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Distributional word clusters vs. words for text categorization
The Journal of Machine Learning Research
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Disambiguating highly ambiguous words
Computational Linguistics - Special issue on word sense disambiguation
Using corpus statistics and WordNet relations for sense identification
Computational Linguistics - Special issue on word sense disambiguation
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Word translation disambiguation using bilingual bootstrapping
Computational Linguistics
Instance based learning with automatic feature selection applied to word sense disambiguation
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Word sense disambiguation by learning from unlabeled data
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
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
Finding predominant word senses in untagged text
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Learning word senses with feature selection and order identification capabilities
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Word sense disambiguation using label propagation based semi-supervised learning
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Semi-supervised Clustering for Word Instances and Its Effect on Word Sense Disambiguation
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
Macro features based text categorization
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Hi-index | 0.00 |
In this paper we investigate an application of feature clustering for word sense disambiguation, and propose a semisupervised feature clustering algorithm. Compared with other feature clustering methods (ex. supervised feature clustering), it can infer the distribution of class labels over (unseen) features unavailable in training data (labeled data) by the use of the distribution of class labels over (seen) features available in training data. Thus, it can deal with both seen and unseen features in feature clustering process. Our experimental results show that feature clustering can aggressively reduce the dimensionality of feature space, while still maintaining state of the art sense disambiguation accuracy. Furthermore, when combined with a semi-supervised WSD algorithm, semi-supervised feature clustering outperforms other dimensionality reduction techniques, which indicates that using unlabeled data in learning process helps to improve the performance of feature clustering and sense disambiguation.