  In anaphora resolution for English, animacy identification can play an
integral role in the application of agreement restrictions between pronouns and
candidates, and as a result, can improve the accuracy of anaphora resolution
systems. In this paper, two methods for animacy identification are proposed and
evaluated using intrinsic and extrinsic measures. The first method is a
rule-based one which uses information about the unique beginners in WordNet to
classify NPs on the basis of their animacy. The second method relies on a
machine learning algorithm which exploits a WordNet enriched with animacy
information for each sense. The effect of word sense disambiguation on the two
methods is also assessed. The intrinsic evaluation reveals that the machine
learning method reaches human levels of performance. The extrinsic evaluation
demonstrates that animacy identification can be beneficial in anaphora
resolution, especially in the cases where animate entities are identified with
high precision.
