Animacy Detection with Voting Models

Joshua L. Moore, Christopher J. C. Burges, Erin Renshaw, Wen-tau Yih

Abstract: Animacy detection is a problem whose solution has been shown to be beneficial for a number of syntactic and semantic tasks. We present a state-of-the-art system for this task which uses a number of simple classifiers with heterogeneous data sources in a voting scheme. We show how this framework can give us direct insight into the behavior of the system, allowing us to more easily diagnose sources of error.