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Abstract:
Disambiguating named entities in natural-language text maps mentions
of ambiguous names onto canonical entities like people or places,
registered in a knowledge base such as DBpedia or YAGO. This paper
presents a robust method for collective disambiguation, by
harnessing context from knowledge bases and using a new form of
coherence graph. It unifies prior approaches into a comprehensive
framework that combines three measures: the prior probability of an
entity being mentioned, the similarity between the contexts of a
mention and a candidate entity, as well as the coherence among
candidate entities for all mentions together. The method builds a
weighted graph of mentions and candidate entities, and computes a
dense subgraph that approximates the best joint mention-entity
mapping. Experiments show that the new method significantly
outperforms prior methods in terms of accuracy, with robust behavior
across a variety of inputs.
