Learning-based Multi-Sieve Co-reference Resolution with Knowledge

Lev-Arie Ratinov, Dan Roth
2012 Conference on Empirical Methods in Natural Language Processing  
We explore the interplay of knowledge and structure in co-reference resolution. To inject knowledge, we use a state-of-the-art system which cross-links (or "grounds") expressions in free text to Wikipedia. We explore ways of using the resulting grounding to boost the performance of a state-of-the-art co-reference resolution system. To maximize the utility of the injected knowledge, we deploy a learningbased multi-sieve approach and develop novel entity-based features. Our end system outperforms
more » ... the state-of-the-art baseline by 2 B 3 F1 points on non-transcript portion of the ACE 2004 dataset. * We thank Nicholas Rizzolo and Kai Wei Chang for their invaluable help with modifying the baseline co-reference system. We thank the anonymous EMNLP reviewers for constructive comments.
dblp:conf/emnlp/RatinovR12 fatcat:4f4z6rtb7naqhnuedx3d6am474