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Semeval-2007 task 02: evaluating word sense induction and discrimination systems
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UMND2: SenseClusters applied to the sense induction task of Senseval-4
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
The role of named entities in web people search
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
SemEval-2010 task 14: Word sense induction & disambiguation
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Significant lexical relationships
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
A quantitative evaluation of global word sense induction
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
Latent semantic word sense induction and disambiguation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
DiSCo '11 Proceedings of the Workshop on Distributional Semantics and Compositionality
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EMNLP '11 Proceedings of the First Workshop on Unsupervised Learning in NLP
MaxMax: a graph-based soft clustering algorithm applied to word sense induction
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Evaluating Word Sense Induction and Disambiguation Methods
Language Resources and Evaluation
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The Duluth-WSI systems in SemEval-2 built word co--occurrence matrices from the task test data to create a second order co--occurrence representation of those test instances. The senses of words were induced by clustering these instances, where the number of clusters was automatically predicted. The Duluth-Mix system was a variation of WSI that used the combination of training and test data to create the co-occurrence matrix. The Duluth-R system was a series of random baselines.