Learning an enriched representation from unlabeled data for protein-protein interaction extraction

Background: Extracting protein-protein interactions from biomedical literature is an important task in biomedical text mining. Supervised machine learning methods have been used with great success in this task but they tend to suffer from data sparseness because of their restriction to obtain knowledge from limited amount of labelled data. In this work, we study the use of unlabeled biomedical texts to enhance the performance of supervised learning for this task. We use feature coupling generalization (FCG)

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Yanpeng Li, Xiaohua Hu, Hongfei Lin, Zhihao Yang
• Keywords: Biomedical Texts, BMCBI 2010, Protein-protein Interaction, Supervised Learning