Coreference systems are driven by syntactic, semantic, and discourse constraints. We present a simple approach which completely modularizes these three aspects. In contrast to much current work, which focuses on learning and on the discourse component, our system is deterministic and is driven entirely by syntactic and semantic compatibility as learned from a large, unlabeled corpus. Despite its simplicity and discourse naivete, our system substantially outperforms all unsupervised systems and most supervised ones. Primary contributions include (1) the presentation of a simpleto-reproduce, high-performing baseline and (2) the demonstration that most remaining errors can be attributed to syntactic and semantic factors external to the coreference phenomenon (and perhaps best addressed by non-coreference systems).
Haghighi and Klein REF proposed an algorithm that modularized syntactic, semantic, and discourse constraints on coreference and learned syntactic and semantic compatibility rules from a large, unlabeled corpus.
Simple Coreference Resolution with Rich Syntactic and Semantic Features
EMNLP
Computer Science