Personal name disambiguation in web search results based on a semi-supervised clustering approach

Authors:
Kazunari Sugiyama;Manabu Okumura
Affiliations:
Precision and Intelligence Laboratory, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan;Precision and Intelligence Laboratory, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan
Venue:
ICADL'07 Proceedings of the 10th international conference on Asian digital libraries: looking back 10 years and forging new frontiers
Year:
2007

Citing 11
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Abstract

Most of the previous works that disambiguate personal names in Web search results often employ agglomerative clustering approaches. In contrast, we have adopted a semi-supervised clustering approach in order to guide the clustering more appropriately. Our proposed semi-supervised clustering approach is novel in that it controls the fluctuation of the centroid of a cluster, and achieved a purity of 0.72 and inverse purity of 0.81, and their harmonic mean F was 0.76.