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Learning-based Multi-Sieve Co-reference Resolution with Knowledge
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
dblp:conf/emnlp/RatinovR12
fatcat:4f4z6rtb7naqhnuedx3d6am474