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Animacy Detection with Voting Models

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.


Publication typeInproceedings
Published inProceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)
PublisherACL – Association for Computational Linguistics
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