Generating Politically-Relevant Event Data
Table 4: Accuracy scores for Category Classifier
['Model', 'Accuracy']
Across the board, it is clear that the character-based ConvNets perform much better than the word-based models. The difference is less drastic for English-language inputs, a 9% difference in accuracy. For Arabic-language inputs, however, the difference is striking. The character model is over 20% more accurate than the word-based model. This is likely due to issues with tokenization and morphology when dealing with the word-based models. Even more impressive is the ability of the Arabic-language models to perform well even with a relatively small corpus of 11,466 coded sentences. These results demonstrate that character-based ConvNets are appropriate and powerful models for the classification of politically-relevant events.