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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the International Joint Conference on Neural Networks : special session on machine learning for text mining |
| Publisher | IEEE Press |
| Publication date | 2004 |
| Pages | 80-85 |
| Publication status | Published - 2004 |