Scaling Wikipedia-based Named Entity Disambiguation to Arbitrary Web Text

Abstract

This paper investigates the named-entity disam- biguation task on the Webidentifying the refer- ent of a string, found on an arbitraryWeb page. The GROUNDER system, introduced in this paper, ad- dresses two challenges not considered by previous work: how to utilize a priori information (e.g., Bill Clinton is more prominent on the Web than Clin- ton County) to improve disambiguation, and how to compose this prior information with contextual evidence. GROUNDER addresses both challenges by leverag- ing the user-contributed knowledge in Wikipedia and providing a novel formulation of the task. On a sample of strings drawn from theWeb, GROUNDER achieves precision of 1.0 at recall 0.34, and preci- sion 0.90 at recall 0.60.