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The paper “Robust Disambiguation of Named Entities in Text” co-written by J. Hoffart, M. A. Yosef, I. Bordino, H. Fürstenau, M. Pinkal, M. Spaniol, S. Thater and G. Weikum has been published in the Proceedings of Conference on Empirical Methods in Natural Language Processing EMNLP 2011, in Edinburgh, Scotland, UK, on July 27–31, 2011.
Disambiguating named entities in naturallanguage 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.