Conference proceeding
A relevance feedback approach for the author name disambiguation problem
Proceedings of the 13th ACM/IEEE-CS joint conference on digital libraries, pp.209-218
JCDL '13
07/22/2013
DOI: 10.1145/2467696.2467709
Abstract
This paper presents a new name disambiguation method that exploits user feedback on ambiguous references across iterations. An unsupervised step is used to define pure training samples, and a hybrid supervised step is employed to learn a classification model for assigning references to authors. Our classification scheme combines the Optimum-Path Forest (OPF) classifier with complex reference similarity functions generated by a Genetic Programming framework. Experiments demonstrate that the proposed method yields better results than state-of-the-art disambiguation methods on two traditional datasets.
Details
- Title: Subtitle
- A relevance feedback approach for the author name disambiguation problem
- Creators
- Thiago GodoiRicardo TorresAriadne CarvalhoMarcos Gonçalves - Universidade Federal de Minas GeraisAnderson Ferreira - Universidade Federal de Ouro PretoWeiguo Fan - Virginia TechEdward Fox - Virginia Tech
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the 13th ACM/IEEE-CS joint conference on digital libraries, pp.209-218
- Publisher
- ACM
- Series
- JCDL '13
- DOI
- 10.1145/2467696.2467709
- ISSN
- 1552-5996
- Language
- English
- Date published
- 07/22/2013
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380505702771
Metrics
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