@CONFERENCE\{IMM2004-02893,
    author       = "R. E. Madsen and S. Sigurdsson and L. K. Hansen",
    title        = "Enhanced Context Recognition by Sensitivity Pruned Vocabularies",
    year         = "2004",
    month        = "aug",
    keywords     = "sensitivity, neural networks, text, classification, dimensionality",
    pages        = "483-486",
    booktitle    = "Proceedings of 17th International Conference on Pattern Recognition ({ICPR} 2004)",
    volume       = "2",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "Cambridge {UK}",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/2893-full.html",
    abstract     = "Language independent `bag-of-words' representations are
surprisingly effective for text classification. The generic {BOW} 
approach is based on a high-dimensional vocabulary which may
reduce the generalization performance of subsequent classifiers,
e.g., based on ill-posed principal component transformations. In
this communication our aim is to study the effect of sensitivity
based pruning of the bag-of-words representation. We consider
neural network based sensitivity maps for determination of term
relevancy, when pruning the vocabularies. With reduced
vocabularies documents are classified using a latent semantic
indexing representation and a probabilistic neural network
classifier. Pruning the vocabularies to approximately 20\% of the
original size, we find consistent context recognition enhancement
for two mid size data-sets for a range of training set sizes. We
also study the applicability of the sensitivity measure for
automated keyword generation."
}