Extending the single words-based document model: a comparison of bigrams and 2-itemsets
Proceedings of the 2006 ACM symposium on Document engineering
Building a topic hierarchy using the bag-of-related-words representation
Proceedings of the 11th ACM symposium on Document engineering
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The co-occurrence of words can make contributions to automatic text classification. However, this information cannot be represented in the feature set when only using primitive features, and can only be partially represented when using n-grams as features. In this paper, we define a novel feature, association feature, to describe this information. In order to make the association features which we selected to be good discriminators, we proposed an approach to create association feature set, including redundancy pruning algorithm and feature selection algorithm. The experiment result shows that the performance of Naïve Bayestext classifier could be improved by using association features, which also means that the selected set of association features can make more contributions to text classification than primitive features, and n-grams.