Vocabulary Pruning for Improved Context Recognition |
Rasmus Elsborg Madsen, Sigurdur Sigurdsson, Lars Kai Hansen, Jan Larsen
|
| Abstract | Language independent `bag-of-words' representations are
surprisingly effective for text classification. The representation
is high dimensional though, containing many non-consistent words
for text categorization. These non-consistent words result in
reduced generalization performance of subsequent classifiers,
e.g., from ill-posed principal component transformations. In this
communication our aim is to study the effect of reducing the least
relevant words from the bag-of-words representation. We consider a
new approach, using neural network based sensitivity maps and
information gain 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. Reducing the bag-of-words
vocabularies with 90%-98%, we find consistent classification
improvement using two mid size data-sets. We also study the
applicability of information gain and sensitivity maps for
automated keyword generation. |
| Keywords | information gain, sensitivity, neural networks, text classification, dimensionality reduction |
| Type | Conference paper [With referee] |
| Conference | Proceedings of the International Joint Conference on Neural Networks |
| Year | 2004 Month August pp. 80-85 |
| Publisher | IEEE Press |
| Note | special session on machine learning for text mining |
| Electronic version(s) | [pdf] |
| BibTeX data | [bibtex] |
| IMM Group(s) | Intelligent Signal Processing |