Unsupervised Learning of Word-Category Guessing Rules
<|reference_start|>Unsupervised Learning of Word-Category Guessing Rules: Words unknown to the lexicon present a substantial problem to part-of-speech tagging. In this paper we present a technique for fully unsupervised statistical acquisition of rules which guess possible parts-of-speech for unknown words. Three complementary sets of word-guessing rules are induced from the lexicon and a raw corpus: prefix morphological rules, suffix morphological rules and ending-guessing rules. The learning was performed on the Brown Corpus data and rule-sets, with a highly competitive performance, were produced and compared with the state-of-the-art.<|reference_end|>
@article{mikheev1996unsupervised,
    title={Unsupervised Learning of Word-Category Guessing Rules},
    author={Andrei Mikheev (HCRC, Edinburgh University)},
    journal={arXiv preprint arXiv:cmp-lg/9604022},
    year={1996},
    archivePrefix={arXiv},
    eprint={cmp-lg/9604022},
    primaryClass={cmp-lg cs.CL}
}