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- W2055963237 abstract "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." @default.
- W2055963237 created "2016-06-24" @default.
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- W2055963237 date "2006-01-01" @default.
- W2055963237 modified "2023-09-24" @default.
- W2055963237 title "Lexicon acquisition for dialectal Arabic using transductive learning" @default.
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- W2055963237 doi "https://doi.org/10.3115/1610075.1610131" @default.
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