Transductive Inference for Text Classification using Support Vector Machines
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K-means clustering via principal component analysis
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Cross-language text classification
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Orthogonal nonnegative matrix t-factorizations for clustering
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Collaborative filtering using orthogonal nonnegative matrix tri-factorization
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Domain adaptation with structural correspondence learning
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Convex and Semi-Nonnegative Matrix Factorizations
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Co-training for cross-lingual sentiment classification
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
A refinement framework for cross language text categorization
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
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Cross-language text classification using structural correspondence learning
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Cross language text classification by model translation and semi-supervised learning
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Using information from the target language to improve crosslingual text classification
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SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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Triplex transfer learning: exploiting both shared and distinct concepts for text classification
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IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Personal and Ubiquitous Computing
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The lack of sufficient labeled Web pages in many languages, especially for those uncommonly used ones, presents a great challenge to traditional supervised classification methods to achieve satisfactory Web page classification performance. To address this, we propose a novel Nonnegative Matrix Tri-factorization (NMTF) based Dual Knowledge Transfer (DKT) approach for cross-language Web page classification, which is based on the following two important observations. First, we observe that Web pages for a same topic from different languages usually share some common semantic patterns, though in different representation forms. Second, we also observe that the associations between word clusters and Web page classes are a more reliable carrier than raw words to transfer knowledge across languages. With these recognitions, we attempt to transfer knowledge from the auxiliary language, in which abundant labeled Web pages are available, to target languages, in which we want classify Web pages, through two different paths: word cluster approximations and the associations between word clusters and Web page classes. Due to the reinforcement between these two different knowledge transfer paths, our approach can achieve better classification accuracy. We evaluate the proposed approach in extensive experiments using a real world cross-language Web page data set. Promising results demonstrate the effectiveness of our approach that is consistent with our theoretical analyses.