Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Cross-Lingual Document Similarity Calculation Using the Multilingual Thesaurus EUROVOC
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Machine Learning
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
NLP and IR approaches to monolingual and multilingual link detection
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
An unsupervised method for word sense tagging using parallel corpora
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Pseudo-aligned multilingual corpora
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Automobile, car and BMW: horizontal and hierarchical approach in social tagging systems
Proceedings of the 2nd ACM workshop on Social web search and mining
A cocktail approach to the VideoCLEF'09 linking task
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
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The crosslingual link detection problem calls for identifying news articles in multiple languages that report on the same news event. This paper presents a novel approach based on constrained clustering. We discuss a general way for constrained clustering using a recent, graph-based clustering framework called correlation clustering. We introduce a correlation clustering implementation that features linear program chunking to allow processing larger datasets. We show how to apply the correlation clustering algorithm to the crosslingual link detection problem and present experimental results that show correlation clustering improves upon the hierarchical clustering approaches commonly used in link detection, and, hierarchical clustering approaches that take constraints into account.