The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Effective transductive learning via objective model selection
Pattern Recognition Letters
Part of speech tagging in context
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Automatic tagging of Arabic text: from raw text to base phrase chunks
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
POS tagging of dialectal Arabic: a minimally supervised approach
Semitic '05 Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages
Explicit learning curves for transduction and application to clustering and compression algorithms
Journal of Artificial Intelligence Research
Discriminative models for semi-supervised natural language learning
SemiSupLearn '09 Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing
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We investigate the problem of learning a part-of-speech (POS) lexicon for a resource-poor language, dialectal Arabic. Developing a high-quality lexicon is often the first step towards building a POS tagger, which is in turn the front-end to many NLP systems. We frame the lexicon acquisition problem as a transductive learning problem, and perform comparisons on three transductive algorithms: Transductive SVMs, Spectral Graph Transducers, and a novel Transductive Clustering method. We demonstrate that lexicon learning is an important task in resource-poor domains and leads to significant improvements in tagging accuracy for dialectal Arabic.