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Uncertainty Sampling-based Active Learning for Protein–protein Interaction Extraction from Biomedical Literature by Baojin Cui; Hongfei Lin; Zhihao Yang is a Computer Science article available to read on EtoBox.

What is Uncertainty Sampling-based Active Learning for Protein–protein Interaction Extraction from Biomedical Literature about?

Protein-protein interaction (PPI) extraction from biomedical literature has become a research focus with the rapid growth of the number of biomedical literature. Many methods have been proposed for PPI extraction including natural language processing techniques and machine learning approaches. One problem of applying machine learning approaches to PPI extraction is that large amounts of data are available but the cost of correctly labeling it prohibits its use. To reduce the amount of human labeling effort while maintaining the PPI extraction performance, the paper presents an uncertainty samplingbased method of active learning (USAL) in a lexical feature-based SVM model to tag the most informative unlabeled samples. In addition, some specific samples are ignored to speed up learning process while maintaining desired accuracy. The experiment results on AIMED and CB corpora show that our method can reduce the labeling by 40% and 20%, respectively, without degrading the performance.

Who reads Uncertainty Sampling-based Active Learning for Protein–protein Interaction Extraction from Biomedical Literature?

Author

Baojin Cui; Hongfei Lin; Zhihao Yang

Publisher

Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0957-4174)

Published

2009

Language

EN

Field

Computer Science (Physical Sciences)