A Scale-Space Theory for Text1st submitted: Jan. 15 2012; revised: Mar. 28 2012.
Table 1: Text classification test accuracy. We compared five models: the bag-of-word vector space models with TF or TFIDF attributes, and the scale-invariant text kernels with BOW 1D (SITK.BOW), LDA 1D (SITK.LDA) and Sentence-level 2D (SITK.Sentence) textual signal. Best results are highlighted in bold.
['Model∖Kernel', 'Linear', 'RBF', 'J-S']
We examined the classification performance of the SVM classifiers that are trained on the one-vs-all splits of the training data, where three types of kernels (i.e., linear (Frobenius), RBF Gaussian and Jensen-Shannon kernels) were considered. The average test accuracy (i.e., Micro-averaged F1) As a reference, the results by BOW representations with TF or TFIDF attributes are also included. For all the three kernel options, the scale-space based SITK models significantly (according to t-test at 0.01 level) outperform the two BOW baselines, while the sentence level SITK performs substantially the best with 7.8% accuracy improvement (i.e., 56% error reduction).