Part-of-Speech Tagging with Minimal Lexicalization
<|reference_start|>Part-of-Speech Tagging with Minimal Lexicalization: We use a Dynamic Bayesian Network to represent compactly a variety of sublexical and contextual features relevant to Part-of-Speech (PoS) tagging. The outcome is a flexible tagger (LegoTag) with state-of-the-art performance (3.6% error on a benchmark corpus). We explore the effect of eliminating redundancy and radically reducing the size of feature vocabularies. We find that a small but linguistically motivated set of suffixes results in improved cross-corpora generalization. We also show that a minimal lexicon limited to function words is sufficient to ensure reasonable performance.<|reference_end|>
@article{savova2003part-of-speech,
    title={Part-of-Speech Tagging with Minimal Lexicalization},
    author={Virginia Savova, Leonid Peshkin},
    journal={arXiv preprint arXiv:cs/0312060},
    year={2003},
    archivePrefix={arXiv},
    eprint={cs/0312060},
    primaryClass={cs.CL cs.LG}
}