Combining Lexical and Syntactic Features for Detecting Content-dense Texts in NewsIn submission to JAIR
Table 4: Binary classification accuracy(%) of 10-fold cross validation on the automatically labeled set for different combinations of features
['[EMPTY]', '[BOLD] Business', '[BOLD] Science', '[BOLD] Sports', '[BOLD] Politics', '[BOLD] General']
The top row in the table corresponds to the baseline, feature-level combination model with all three classes of features. Rows 2-4 correspond to decision-level models with the three different classifier combinations. As in previous tables, the first four columns correspond to domain-specific models, and the last column shows the results for the general, domain-independent model. Combination classifiers based on all three features in decision-level combination still has the highest accuracy, showing that each of the three representations contributes to the improved performance of the classifier. The domain independent features, MRC+PR with decision-level combination shows a competitive results too, suggesting that the mutual information representation is the one that could be removed with least degradation in performance. The accuracies are just slightly lower than the best, 0.5% lower for the business domain for example. Increasing the number of training instances barely changes the performance after this point.