SANAPHOR: Ontology-Based Coreference Resolution

We tackle the problem of resolving coreferences in textual content by leveraging Semantic Web techniques. Specifically, we focus on noun phrases that coreference identifiable entities that appear in the text; the challenge in this context is to improve the coreference resolution by leveraging potential semantic annotations that can be added to the identified mentions. Our system, SANAPHOR, first applies state-of-the-art techniques to extract entities, noun phrases, and candidate coreferences. Then, we propose an approach to type noun phrases using an inverted index built on top of a Knowledge Graph e.g., DBpedia. Finally, we use the semantic relatedness of the introduced types to improve the state-of-the-art techniques by splitting and merging coreference clusters. We evaluate SANAPHOR on CoNLL datasets, and show how our techniques consistently improve the state of the art in coreference resolution.