Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/handle/123456789/4334
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dc.contributor.authorFerreira, Anderson Almeida-
dc.contributor.authorMachado, Tales Mota-
dc.contributor.authorGonçalves, Marcos André-
dc.date.accessioned2015-01-22T14:56:35Z-
dc.date.available2015-01-22T14:56:35Z-
dc.date.issued2012-
dc.identifier.citationFERREIRA, A. A.; MACHADO, T. M.; GONÇALVES, M. A. Improving author name disambiguation with user relevance feedback. Journal of Information and Data Management - JIDM, v. 3, p. 332-347, 2012. Disponível em: <https://seer.lcc.ufmg.br/index.php/jidm/article/view/200/135>. Acesso em: 21 jan. 2015.pt_BR
dc.identifier.issn21787107-
dc.identifier.urihttp://www.repositorio.ufop.br/handle/123456789/4334-
dc.description.abstractAuthor name ambiguity in the context of bibliographic citations is a very hard problem. It occurs when there are citation records of a same author under distinct names or when there exists citation records belonging to distinct authors with very similar names. Among the several methods proposed in the literature, the most effective ones are those that perform a direct assignment of the records to their respective authors by means of the application of supervised machine learning techniques. However, those methods usually need large amounts of labeled training examples to properly disambiguate the author names. To deal with this issue, in previous work, we have proposed a method that automatically obtains and labels the training examples, showing competitive performance compared to representative author name disambiguation methods. In this work, we propose to improve our previous method by exploiting user relevance feedback. In more details we select a very small portion of the citation records for which our method was mostly unsure about the correct authorship and ask the administrators for labeling them. This feedback is then used to improve the effectiveness of the whole process. In our experimental evaluation, we observed that with a very small labeling effort (usually around 5% of the records), the overall disambiguation effectiveness improves by almost 10% on average, with gains of up to 61% in some of the largest ambiguous groups.pt_BR
dc.language.isoen_USpt_BR
dc.subjectBibliographic citationpt_BR
dc.subjectDigital librarypt_BR
dc.subjectName disambiguationpt_BR
dc.subjectRelevance feedbackpt_BR
dc.titleImproving author name disambiguation with user relevance feedback.pt_BR
dc.typeArtigo publicado em periodicopt_BR
dc.rights.licenseCopyright 2012 Permission to copy without fee all or part of the material printed in JIDM is granted provided that the copies are not made or distributed for commercial advantage, and that notice is given that copying is by permission of the Sociedade Brasileira de Computação. Fonte: Informação contida no artigo.pt_BR
Appears in Collections:DECOM - Artigos publicados em periódicos

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