Understanding and Detecting Supporting Arguments of Diverse Types
Table 2: Results for argument type prediction. One-vs-rest classifiers are learned for Log-linear models.
['[EMPTY]', '[BOLD] Acc', '[BOLD] F1']
We utilize Log-linear model for argument type prediction with one-vs-rest setup. Three baselines are considered: (1) random guess, (2) majority class, and (3) unigrams and bigrams as features for Log-linear model. Identified supporting arguments are used for experiments, and divided into training set (50%), validation set (25%) and test set (25%). As can be seen, arguments of study and factual tend to contain more concrete words and named entities. Arguments of opinion mention more person names, which implies that expert opinions are commonly quoted.