In this paper, we propose a novel way to include unsupervised feature selection methods in probabilistic taxonomy learning models. We leverage on the computation of logistic regression to exploit unsupervised feature selection of singular value decomposition (SVD). Experiments show that this way of using SVD for feature selection positively affects performances.

Fallucchi, F., Zanzotto, F.m. (2009). Singular value decomposition for Feature Selection in Taxonomy Learning. In Proceedings of the Conference on Recent Advances on Natural Language Processing. John Benjamins.

Singular value decomposition for Feature Selection in Taxonomy Learning

FALLUCCHI, FRANCESCA;ZANZOTTO, FABIO MASSIMO
2009-01-01

Abstract

In this paper, we propose a novel way to include unsupervised feature selection methods in probabilistic taxonomy learning models. We leverage on the computation of logistic regression to exploit unsupervised feature selection of singular value decomposition (SVD). Experiments show that this way of using SVD for feature selection positively affects performances.
International Conference on Recent Advances in Natural Language Processing, RANLP-2009
Rilevanza internazionale
contributo
2009
2009
Settore ING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI
Settore INF/01 - INFORMATICA
English
http://www.aclweb.org/anthology-new/R/R09/R09-1016.pdf
Intervento a convegno
Fallucchi, F., Zanzotto, F.m. (2009). Singular value decomposition for Feature Selection in Taxonomy Learning. In Proceedings of the Conference on Recent Advances on Natural Language Processing. John Benjamins.
Fallucchi, F; Zanzotto, Fm
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/162161
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