Combining Lexical and Syntactic Features for Detecting Content-dense Texts in NewsIn submission to JAIR
Table 3: Binary classification accuracy(%) of 10-fold cross validation on the automatically labeled set for different classes of features and two fusion models
['[EMPTY]', '[BOLD] Business', '[BOLD] Science', '[BOLD] Sports', '[BOLD] Politics', '[BOLD] General']
Because of the way the data was labeled, the two classes are of equal size, with 50% accuracy as the random baseline. The top three rows in the table corresponds to a system trained with only one class of features. The last two rows shows the results for the two combination systems. The columns correspond to the domains we study—business, science, sports and politics. The domain-specific models were trained and tested only on the data from the given domain and the results are shown in the first four columns. The general, domain-independent model is trained and tested on the combined dataset and the last column shows its performance.