Can chinese web pages be classified with english data source?

By Xiao Ling, Gui-Rong Xue, Wenyuan Dai, Yun Jiang, Qiang Yang, Yong Yu. 2008

129 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiaojun Wan
2. Qiang Yang
3. Feiping Nie
4. Heng Huang
5. Jialin Pan
6. Hua Wang
7. Sinno Jialin Pan
8. Yuqiang Chen

In Information Systems

1. Xiaojun Wan
2. Qiang Yang
3. Jialin Pan
4. Feiping Nie
5. Gui-Rong Xue
6. Chris H. Q. Ding
7. Heng Huang
8. Yong Yu

In Molecular Biology

1. Xiaojun Wan
2. Feiping Nie
3. Heng Huang
4. Qiang Yang
5. Hua Wang
6. Chris H. Q. Ding
7. Jialin Pan
8. Hanchuan Peng

Citations in the corpus

• Transfer learning for cross-company software defect prediction (2012, background): “Transfer learning technique also works well on many fields such as image classification [19], name-entity recognition [20], web page translation [21], natural language processing [22], and email spam filtering [23].”
• Co-Training for Cross-Lingual Sentiment Classification (2009, methodology): “A few novel models have been proposed to address the problem, e.g. the EM-based algo-rithm (Rigutini et al., 2005), the information bottleneck approach (Ling et al., 2008), the multi-lingual domain models (Gliozzo and Strapparava, 2005), etc.”
• Cross Language Text Classification via Subspace Co-regularized Multi-view Learning (2012, methodology): “Various methods have been proposed to tackle these issues on translated data to increase cross language text classification accuracy, including an information bottleneck method (Ling et al., 2008), EM-based model translation techniques (Shi et al.”
• Cross-language web page classification via dual knowledge transfer using nonnegative matrix tri-factorization (2011, methodology): “The first difficulty in cross-language Web page classification is due to cultural discrepancies, which heavily impact the classification performance in spite of a perfect translation [15, 19].”
• Bilingual Co-Training for Sentiment Classification of Chinese Product Reviews (2011, methodology): “…have been proposed to address the problem—for example, the EM-based algorithm (Rigutini, Maggini, and Liu 2005), the information bottleneck approach (Ling et al. 2008), multilingual domain models (Gliozzo and Strapparava 2005), and the structural correspondence learning approach (Prettenhofer and…”
• Heterogeneous Transfer Learning for Image Clustering via the SocialWeb (2009, methodology): “In (Ling et al., 2008) we have shown how to classify the Chinese text using English text as the training data.”
• Feature-based transfer learning with real-world applications (2010, background): “[109] proposed an information-theoretic approach for transfer learning to address the cross-language classification problem.”