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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Deep Pivot-Based Modeling for Cross-language Cross-domain Transfer with Minimal Guidance
Yftah Ziser,Roi Reichart +1 more
TL;DR: This work proposes a framework that builds on pivot-based learning, structure-aware Deep Neural Networks and bilingual word embeddings, with the goal of training a model on labeled data from one language, domain so that it can be effectively applied to another (language, domain) pair.
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Transfer Learning for Cross-Dataset Recognition: A Survey
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Proceedings Article
Modeling Review Argumentation for Robust Sentiment Analysis
TL;DR: It is claimed that even a shallow model of the argumentation of a text allows for an effective and more robust classification, while providing intuitive explanations of the classification results.
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Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
Edoardo Maria Ponti,Helen O'Horan,Yevgeni Berzak,Ivan Vulić,Roi Reichart,Thierry Poibeau,Ekaterina Shutova,Anna Korhonen +7 more
TL;DR: The authors presented an extensive literature survey on the use of typological information in the development of NLP techniques and showed that to date, using information in existing typological databases has resulted in consistent but modest improvements in system performance, due to both intrinsic limitations of databases and under-employment of the typological features included in them.
Journal Article
Distributional Correspondence Indexing for Cross-Lingual and Cross-Domain Sentiment Classification.
TL;DR: In this paper, a distributional correspondence indexing (DCI) method is proposed for domain adaptation in sentiment classification, which derives term representations in a vector space common to both domains where each dimension re ects its distributional correspondences to a pivot, i.e., a highly predictive term that behaves similarly across domains.
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Proceedings ArticleDOI
Thumbs up? Sentiment Classification using Machine Learning Techniques
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Proceedings Article
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