Joshua L. Moore, Christopher J.C. Burges, Erin Renshaw, and Wen-tau Yih
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.
|Published in||Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)|
|Publisher||ACL – Association for Computational Linguistics|