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CIKM
2009
Springer

Improving binary classification on text problems using differential word features

8 years 10 months ago
Improving binary classification on text problems using differential word features
We describe an efficient technique to weigh word-based features in binary classification tasks and show that it significantly improves classification accuracy on a range of problems. The most common text classification approach uses a document's ngrams (words and short phrases) as its features and assigns feature values equal to their frequency or TFIDF score relative to the training corpus. Our approach uses values computed as the product of an ngram's document frequency and the difference of its inverse document frequencies in the positive and negative training sets. While this technique is remarkably easy to implement, it gives a statistically significant improvement over the standard bag-ofwords approaches using support vector machines on a range of classification tasks. Our results show that our technique is robust and broadly applicable. We provide an analysis of why the approach works and how it can generalize to other domains and problems. This is a preprint of a sho...
Justin Martineau, Tim Finin, Anupam Joshi, Shamit
Added 14 Aug 2010
Updated 14 Aug 2010
Type Conference
Year 2009
Where CIKM
Authors Justin Martineau, Tim Finin, Anupam Joshi, Shamit Patel
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