Unsupervised Learning of Word-Category Guessing Rules
| dc.creator | Mikheev, Andrei | |
| dc.date | 1996-04-30 | |
| dc.date.accessioned | 2026-07-07T09:10:12Z | |
| dc.date.available | 2026-07-07T09:10:12Z | |
| dc.description | 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. | |
| dc.description | 8 pages, LaTeX (aclap.sty for ACL-96); Proceedings of ACL-96 Santa Cruz, USA; also see cmp-lg/9604025 | |
| dc.identifier | https://arxiv.org/abs/cmp-lg/9604022 | |
| dc.identifier | http://arxiv.org/abs/cmp-lg/9604022 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/151287 | |
| dc.subject | Computation and Language | |
| dc.title | Unsupervised Learning of Word-Category Guessing Rules | |
| dc.type | text |