Open AccessProceedings Article
Cross-Language Text Classification Using Structural Correspondence Learning
Peter Prettenhofer,Benno Stein +1 more
- pp 1118-1127
TLDR
A new approach to cross-language text classification that builds on structural correspondence learning, a recently proposed theory for domain adaptation, is presented, using unlabeled documents, along with a simple word translation oracle, in order to induce task-specific, cross-lingual word correspondences.Abstract:
We present a new approach to cross-language text classification that builds on structural correspondence learning, a recently proposed theory for domain adaptation. The approach uses unlabeled documents, along with a simple word translation oracle, in order to induce task-specific, cross-lingual word correspondences. We report on analyses that reveal quantitative insights about the use of unlabeled data and the complexity of inter-language correspondence modeling.
We conduct experiments in the field of cross-language sentiment classification, employing English as source language, and German, French, and Japanese as target languages. The results are convincing; they demonstrate both the robustness and the competitiveness of the presented ideas.read more
Citations
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TL;DR: It is shown that addressing the domain gap is crucial in XLU and state-of-the-art cross-lingual methods are combined with recently proposed methods for weakly supervised learning such as unsupervised pre-training and unsuper supervised data augmentation to simultaneously close both the language gap and thedomain gap.
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Graph-based semi-supervised learning for cross-lingual sentiment classification
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References
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Proceedings ArticleDOI
Thumbs up? Sentiment Classification using Machine Learning Techniques
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Proceedings Article
Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification
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