We examine the task of resolving complex cases of definite pronouns, specifically those for which traditional linguistic constraints on coreference (e.g., Binding Constraints, gender and number agreement) as well as commonly-used resolution heuristics (e.g., string-matching facilities, syntactic salience) are not useful. Being able to solve this task has broader implications in artificial intelligence: a restricted version of it, sometimes referred to as the Winograd Schema Challenge, has been suggested as a conceptually and practi- cally appealing alternative to the Turing Test. We employ a knowledge-rich approach to this task, which yields a pronoun resolver that out- performs state-of-the-art resolvers by nearly 18 points in accuracy on our dataset.

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