Figure 1 pictorially demonstrates these results by plotting the difference between the mean values of each commitment feature (here normalized by token count) of superiors vs. subordinates, as a percentage of mean feature value of the corresponding commitment feature for superiors.
tHR thread structure (e.g., reply rate) DIA Dialog act tagging (e.g., request count) ODP Overt displays of power LEX Lexical ngrams (lemma, POS, mixed ngrams) None of the features used in POWERPREDIC-tOR use information from the parse trees of sentences in the text in order to accurately obtain the belief labels, deep dependency parse based features are critical (Prabhakaran et al., 2010).