Transfer Learning for Cross-Language Text Categorization through Active Correspondences Construction

Authors

  • Joey Zhou Institute of High Performance Computing
  • Sinno Pan Nanyang Technological University
  • Ivor Tsang University of Technology
  • Shen-Shyang Ho Nanyang Technological University

DOI:

https://doi.org/10.1609/aaai.v30i1.10211

Keywords:

Heterogeneous Transfer Learning, Active Learning, Cross-Language Text Categorization

Abstract

Most existing heterogeneous transfer learning (HTL) methods for cross-language text classification rely on sufficient cross-domain instance correspondences to learn a mapping across heterogeneous feature spaces, and assume that such correspondences are given in advance. However, in practice, correspondences between domains are usually unknown. In this case, extensively manual efforts are required to establish accurate correspondences across multilingual documents based on their content and meta-information. In this paper, we present a general framework to integrate active learning to construct correspondences between heterogeneous domains for HTL, namely HTL through active correspondences construction (HTLA). Based on this framework, we develop a new HTL method. On top of the new HTL method, we further propose a strategy to actively construct correspondences between domains. Extensive experiments are conducted on various multilingual text classification tasks to verify the effectiveness of HTLA.

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Published

2016-03-02

How to Cite

Zhou, J., Pan, S., Tsang, I., & Ho, S.-S. (2016). Transfer Learning for Cross-Language Text Categorization through Active Correspondences Construction. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.10211

Issue

Section

Technical Papers: Machine Learning Methods