Graph ranking for collective Named Entity Disambiguation

Abstract

Named Entity Disambiguation (NED) refers to the task of mapping different named entity mentions in running text to their correct interpretations in a specific knowledge base (KB). This paper presents a collective disambiguation approach using a graph model. All possible NE candidates are represented as nodes in the graph and associations between different candidates are represented by edges between the nodes. Each node has an initial confidence score, e.g. entity popularity. Page-Rank is used to rank nodes and the final rank is combined with the initial confidence for candidate selection. Experiments on 27,819 NE textual mentions show the effectiveness of using Page-Rank in conjunction with initial confidence: 87% accuracy is achieved, outperforming both baseline and state-of-the-art approaches. © 2014 Association for Computational Linguistics.