Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Machine Learning
Active Learning for Natural Language Parsing and Information Extraction
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Automatic Extraction of Biological Information from Scientific Text: Protein-Protein Interactions
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Extracting Protein-Protein Interaction Information from Biomedical Text with SVM
IEICE - Transactions on Information and Systems
Multi-criteria-based active learning for named entity recognition
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
BioPPIExtractor: A protein-protein interaction extraction system for biomedical literature
Expert Systems with Applications: An International Journal
Comparative experiments on learning information extractors for proteins and their interactions
Artificial Intelligence in Medicine
Remote sensing image segmentation by active queries
Pattern Recognition
Cross-domain video concept detection: A joint discriminative and generative active learning approach
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
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 sampling-based 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.