JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2017, Vol. 52 ›› Issue (6): 24-31.doi: 10.6040/j.issn.1671-9352.5.2016.023

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Model selection with likelihood ratio for sequence transfer learning

SUN Shi-chang1,2, LIN Hong-fei1, MENG Jia-na2*, LIU Hong-bo3   

  1. 1. School of Computer, Dalian University of Technology, Dalian 116023, Liaoning, China;
    2. School of Computer, Dalian Minzu University, Dalian 116600, Liaoning, China;
    3. Information Science and Technology College, Dalian Maritime University, Dalian 116026, Liaoning, China
  • Received:2016-10-11 Online:2017-06-20 Published:2017-06-21

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Abstract: To solve the under-adaptation problem of transfer learning,in this paper the granular model is used as a set of candidate models, and labeling rules contained in minor for target domain models is introduced by a model selection method. We propose a Likelihood Ratio based Model Selection method(LRMS)for the inference of granular model, which implements the fusion of minor models with the granular model. LRMS keeps the single-path calculating of Viterbi-based sequence labeling model, which avoid the violation of contextual connections. In empirical experiments on part-of-speech tagging, LRMS improves the accuracy in every transfer learning task, therefore, the effectiveness of LRMS in solving the under-adaptation problem is verified.

Key words: transfer learning, likelihood ratio, part-of-speech tagging, model selection

CLC Number: 

  • TP391
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