Affiliations: [a] School of Software, Beihang University, Beijing, China
| [b] Tandon School of Engineering, New York University, NY, USA
Correspondence:
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Corresponding author. Huobin Tan, School of Software, Beihang University, Beijing, China. Tel.: +8601082338517; E-mail: [email protected].
Abstract: The name disambiguation task is designed to solve the name ambiguity problem of documents of multiple persons who have the same name with one another. The task aims to partition all the publications belonging to multiple person with the same name and realize that each decomposed partition is composed of publications of a unique person. Many works on name disambiguation task have a common feature that clustering method is usually used in the last step. The paper presents a complementary study to these works from another point of view. Based on the idea that documents with strong association relationships are likely to belong to the same author, this paper proposes a method of discovering meta clusters by graph partition with a heuristic rule to improve these clustering-based works. Specially, different from these works, this work uses clustering ensemble method instead of clustering method in the last step. Experimental results on a real-life dataset show that the improved method has satisfactory performance compared with the clustering-based baseline method.
Keywords: Name disambiguation, meta clusters, clustering ensemble, graph partition
