Lexicon Acquisition For Dialectal Arabic Using Transductive Learning


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.