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Coreference resolution is a very important problem for many NLP applications. Most existing methods for coreference resolution make use of attribute-value features over pairs of noun phrases, which can't adequately describe the coreference conditions and properties between noun phrases. In this paper, we present a new approach to coreference resolution by combining Inductive Logic Programming (ILP)...
Artificial neural networks (ANN) have demonstrated good predictive performance in a wide range of applications. They are, however, not considered sufficient for knowledge representation because of their inability to represent the reasoning process succinctly. This paper proposes a novel methodology Gyan that represents the knowledge of a trained network in the form of restricted first-order predicate...
This paper describes FOIL, a system that learns Horn clauses from data expressed as relations. FOIL is based on ideas that have proved effective in attribute-value learning systems, but extends them to a first-order formalism. This new system has been applied successfully to several tasks taken from the machine learning literature.
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