The Effect of Different Writing Tasks on Linguistic Style:A Case Study of the ROC Story Cloze Task
Table 4: Results on Experiment 1 with different subsets of features.
['[BOLD] Feature Type', '[BOLD] Accuracy']
A natural question that follows from this study is which style features are most helpful in detecting the underlying task an author was asked to perform. To answer this question, we re-ran Experiment 1 with different sub-groups of features. Results show that character n-grams are the most effective style predictors, reaching within 0.6% of the full model, but that word n-grams also capture much of the signal, yielding 61.2%, which is only 3.3% worse than the full model. These findings are in line with previous work that used character n-grams along with other types of features to predict writing style Schwartz et al.