Automatic cluster stopping with criterion functions and the gap statistic
NAACL-Demonstrations '06 Proceedings of the 2006 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume: demonstrations
Two graph-based algorithms for state-of-the-art WSD
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Semeval-2007 task 02: evaluating word sense induction and discrimination systems
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Name discrimination by clustering similar contexts
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Word Sense Induction Using Graphs of Collocations
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
KCDC: Word sense induction by using grammatical dependencies and sentence phrase structure
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Duluth-WSI: SenseClusters applied to the sense induction task of SemEval-2
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Word sense induction & disambiguation using hierarchical random graphs
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Nonparametric Bayesian word sense induction
TextGraphs-6 Proceedings of TextGraphs-6: Graph-based Methods for Natural Language Processing
Evaluating Word Sense Induction and Disambiguation Methods
Language Resources and Evaluation
| Hi-index | 0.00 |
SenseClusters is a freely--available open--source system that served as the University of Minnesota, Duluth entry in the Senseval-4 sense induction task. For this task SenseClusters was configured to construct representations of the instances to be clustered using the centroid of word cooccurrence vectors that replace the words in an instance. These instances are then clustered using k--means where the number of clusters is discovered automatically using the Adapted Gap Statistic. In these experiments SenseClusters did not use any information outside of the raw untagged text that was to be clustered, and no tuning of the system was performed using external corpora.