Paper: Learning the Peculiar Value of Actions

ACL ID S14-1008
Title Learning the Peculiar Value of Actions
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

We consider the task of automatically es- timating the value of human actions. We cast the problem as a supervised learning- to-rank problem between pairs of action descriptions. We present a large, novel data set for this task which consists of challenges from the I Will If You Will Earth Hour challenge. We show that an SVM ranking model with simple linguistic features can accurately predict the relative value of actions.