UM
Substructural Regularization with Data-Sensitive Granularity for Sequence Transfer Learning
Sun S.3; Liu H.1; Meng J.3; Chen C.L.P.4; Yang Y.1
2018-06-01
Source PublicationIEEE Transactions on Neural Networks and Learning Systems
ISSN21622388 2162237X
Volume29Issue:6Pages:2545-2557
AbstractSequence transfer learning is of interest in both academia and industry with the emergence of numerous new text domains from Twitter and other social media tools. In this paper, we put forward the data-sensitive granularity for transfer learning, and then, a novel substructural regularization transfer learning model (STLM) is proposed to preserve target domain features at substructural granularity in the light of the condition of labeled data set size. Our model is underpinned by hidden Markov model and regularization theory, where the substructural representation can be integrated as a penalty after measuring the dissimilarity of substructures between target domain and STLM with relative entropy. STLM can achieve the competing goals of preserving the target domain substructure and utilizing the observations from both the target and source domains simultaneously. The estimation of STLM is very efficient since an analytical solution can be derived as a necessary and sufficient condition. The relative usability of substructures to act as regularization parameters and the time complexity of STLM are also analyzed and discussed. Comprehensive experiments of part-of-speech tagging with both Brown and Twitter corpora fully justify that our model can make improvements on all the combinations of source and target domains.
KeywordData-sensitive granularity hidden Markov model (HMM) relative entropy (RE) sequence transfer learning substructural regularization
DOI10.1109/TNNLS.2016.2638321
URLView the original
Language英語
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.Dalian Maritime University
2.Dalian Minzu University
3.Dalian University of Technology
4.Universidade de Macau
Recommended Citation
GB/T 7714
Sun S.,Liu H.,Meng J.,et al. Substructural Regularization with Data-Sensitive Granularity for Sequence Transfer Learning[J]. IEEE Transactions on Neural Networks and Learning Systems,2018,29(6):2545-2557.
APA Sun S.,Liu H.,Meng J.,Chen C.L.P.,&Yang Y..(2018).Substructural Regularization with Data-Sensitive Granularity for Sequence Transfer Learning.IEEE Transactions on Neural Networks and Learning Systems,29(6),2545-2557.
MLA Sun S.,et al."Substructural Regularization with Data-Sensitive Granularity for Sequence Transfer Learning".IEEE Transactions on Neural Networks and Learning Systems 29.6(2018):2545-2557.
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