Automatic cluster stopping with criterion functions and the gap statistic

Authors:
Ted Pedersen;Anagha Kulkarni
Affiliations:
University of Minnesota, Duluth, Duluth, MN;University of Minnesota, Duluth, Duluth, MN
Venue:
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
Year:
2006

Citing 2
Cited 14

Clustering Algorithms

Clustering Algorithms
Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms

ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence

Using hidden Markov random fields to combine distributional and pattern-based word clustering

COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Clustering and matching headlines for automatic paraphrase acquisition

ENLG '09 Proceedings of the 12th European Workshop on Natural Language Generation
UMND2: SenseClusters applied to the sense induction task of Senseval-4

SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Clustering technique in multi-document personal name disambiguation

ACLstudent '09 Proceedings of the ACL-IJCNLP 2009 Student Research Workshop
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
The effect of different context representations on word sense discrimination in biomedical texts

Proceedings of the 1st ACM International Health Informatics Symposium
Towards a framework for developing semantic relatedness reference standards

Journal of Biomedical Informatics
Finding the optimal number of clusters for word sense disambiguation

TSD'11 Proceedings of the 14th international conference on Text, speech and dialogue
Measuring the impact of sense similarity on word sense induction

EMNLP '11 Proceedings of the First Workshop on Unsupervised Learning in NLP
Methods of estimating the number of clusters for person cross document coreference task

CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
GANC: Greedy agglomerative normalized cut for graph clustering

Pattern Recognition
Evaluating unsupervised ensembles when applied to word sense induction

ACL '12 Proceedings of ACL 2012 Student Research Workshop
Evaluating Word Sense Induction and Disambiguation Methods

Language Resources and Evaluation

Quantified Score

Hi-index	0.00

Visualization

Abstract

SenseClusters is a freely available system that clusters similar contexts. It can be applied to a wide range of problems, although here we focus on word sense and name discrimination. It supports several different measures for automatically determining the number of clusters in which a collection of contexts should be grouped. These can be used to discover the number of senses in which a word is used in a large corpus of text, or the number of entities that share the same name. There are three measures based on clustering criterion functions, and another on the Gap Statistic.