Paper: Lexicon Acquisition For Dialectal Arabic Using Transductive Learning

ACL ID W06-1647
Title Lexicon Acquisition For Dialectal Arabic Using Transductive Learning
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2006
Authors

We investigate the problem of learn- ing a part-of-speech (POS) lexicon for a resource-poor language, dialectal Arabic. Developing a high-quality lexicon is often the rst step towards building a POS tag- ger, which is in turn the front-end to many NLP systems. We frame the lexicon ac- quisition problem as a transductive learn- ing problem, and perform comparisons on three transductive algorithms: Trans- ductive SVMs, Spectral Graph Transduc- ers, and a novel Transductive Clustering method. We demonstrate that lexicon learning is an important task in resource- poor domains and leads to signi cant im- provements in tagging accuracy for dialec- tal Arabic.