Predicting Fine-grained Social Roles with Selectional Preferences

Selectional preferences, the tendencies of predicates to select for certain semantic classes of arguments, have been successfully applied to a number of tasks in computational linguistics including word sense disambiguation, semantic role labeling, relation extraction, and textual inference. Here we leverage the information encoded in selectional preferences to the task of predicting fine-grained categories of authors on the social media platform Twitter. First person uses of verbs that select for a given social role as subject (e.g. I teach ... for teacher) are used to quickly build up binary classifiers for that role.