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 resourcepoor domains and leads to significant improvements in tagging accuracy for dialectal Arabic.

• Type: Conference
• Year: 2006
• Where: EMNLP
• Authors: Kevin Duh, Katrin Kirchhoff
• Keywords: Dialectal Arabic, EMNLP 2006, EMNLP 2007, Lexicon, Transductive Learning Problem