Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/handle/123456789/1727
Title: Cost-effective on-demand associative author name disambiguation.
Authors: Veloso, Adriano Alonso
Ferreira, Anderson Almeida
Gonçalves, Marcos André
Laender, Alberto Henrique Frade
Meira Júnior, Wagner
Keywords: Machine learning
Digital libraries
Author name disambiguation
Associative methods
Lazy strategies
Issue Date: 2012
Citation: VELOSO, A. A. et al. Cost-effective on-demand associative author name disambiguation. Information Processing and Management, v. 48, n. 4, p. 680-697, 2012. Disponível em: <https://www.sciencedirect.com/science/article/pii/S0306457311000847>. Acesso em: 22 out. 2012
Abstract: Authorship disambiguation is an urgent issue that affects the quality of digital library ser-vices and for which supervised solutions have been proposed, delivering state-of-the-art effectiveness. However, particular challenges such as the prohibitive cost of labeling vast amounts of examples (there are many ambiguous authors), the huge hypothesis space (there are several features and authors from which many different disambiguation func-tions may be derived), and the skewed author popularity distribution (few authors are very prolific, while most appear in only few citations), may prevent the full potential of such techniques. In this article, we introduce an associative author name disambiguation approach that identifies authorship by extracting, from training examples, rules associating citation features (e.g., coauthor names, work title, publication venue) to specific authors. As our main contribution we propose three associative author name disambiguators: (1) EAND (Eager Associative Name Disambiguation), our basic method that explores associa-tion rules for name disambiguation; (2) LAND (Lazy Associative Name Disambiguation), that extracts rules on a demand-driven basis at disambiguation time, reducing the hypoth-esis space by focusing on examples that are most suitable for the task; and (3) SLAND (Self-Training LAND), that extends LAND with self-training capabilities, thus drastically reducing the amount of examples required for building effective disambiguation functions, besides being able to detect novel/unseen authors in the test set. Experiments demonstrate that all our disambigutators are effective and that, in particular, SLAND is able to outperform state-of-the-art supervised disambiguators, providing gains that range from 12% to more than 400%, being extremely effective and practical.
URI: http://www.repositorio.ufop.br/handle/123456789/1727
metadata.dc.rights.license: O periódico Information Processing and Management concede permissão para depósito do artigo no Repositório Institucional da UFOP. Número da licença: 3291850076753.
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