An elementary proof of a theorem of Johnson and Lindenstrauss
Random Structures & Algorithms
Database-friendly random projections: Johnson-Lindenstrauss with binary coins
Journal of Computer and System Sciences - Special issu on PODS 2001
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Boosting margin based distance functions for clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A sparse Johnson: Lindenstrauss transform
Proceedings of the forty-second ACM symposium on Theory of computing
SemEval-2012 task 6: a pilot on semantic textual similarity
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
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We propose a semantic similarity learning method based on Random Indexing (RI) and ranking with boosting. Unlike classical RI, we use only those context vector features that are informative for the semantics modeled. Despite ignoring text preprocessing and dispensing with semantic resources, the approach was ranked as high as 22nd among 89 participants in the SemEval-2012 Task6: Semantic Textual Similarity.