An Active Transfer Learning Framework for Protein-Protein Interaction Extraction

Lishuang LI  Xinyu HE  Jieqiong ZHENG  Degen HUANG  Fuji REN  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.2   pp.504-511
Publication Date: 2018/02/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDP7232
Type of Manuscript: PAPER
Category: Natural Language Processing
Keyword: 
protein-protein interaction,  TrAdaBoost,  actively transfer learning,  relative distribution,  

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Summary: 
Protein-Protein Interaction Extraction (PPIE) from biomedical literatures is an important task in biomedical text mining and has achieved great success on public datasets. However, in real-world applications, the existing PPI extraction methods are limited to label effort. Therefore, transfer learning method is applied to reduce the cost of manual labeling. Current transfer learning methods suffer from negative transfer and lower performance. To tackle this problem, an improved TrAdaBoost algorithm is proposed, that is, relative distribution is introduced to initialize the weights of TrAdaBoost to overcome the negative transfer caused by domain differences. To make further improvement on the performance of transfer learning, an approach combining active learning with the improved TrAdaBoost is presented. The experimental results on publicly available PPI corpora show that our method outperforms TrAdaBoost and SVM when the labeled data is insufficient,and on document classification corpora, it also illustrates that the proposed approaches can achieve better performance than TrAdaBoost and TPTSVM in final, which verifies the effectiveness of our methods.