Combining Lexical and Syntactic Features for Detecting Content-dense Texts in NewsIn submission to JAIR
Table 10: Binary classification accuracies(%) on AMT annotated datasets for models trained on heuristically labeled data.
['[BOLD] In-domain', 'Business', 'Science', 'Sports', 'Politics', 'Average']
As in previous tables, row 1 and 2 represent the results from domain models and the domain-independent models respectively. Rows 3 to 4 show results for the two baselines. Our classifiers outperform the baselines by a large margin except for politics in the domain-independent labels, where the baseline that considers all leads to be content dense works best. Overall however, the results show that the baseline of assuming all leads are content-dense performs poorly and the proposed approaches significantly improve the accuracies.