TAKTAG: Two-phase learning method for hybrid statistical/rule-based part-of-speech disambiguation

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dc.creatorLee, Geunbae
dc.creatorLee, Jong-Hyeok
dc.creatorShin, Sanghyun
dc.date1995-04-26
dc.date1995-05-28
dc.date.accessioned2026-07-07T08:58:40Z
dc.date.available2026-07-07T08:58:40Z
dc.descriptionBoth statistical and rule-based approaches to part-of-speech (POS) disambiguation have their own advantages and limitations. Especially for Korean, the narrow windows provided by hidden markov model (HMM) cannot cover the necessary lexical and long-distance dependencies for POS disambiguation. On the other hand, the rule-based approaches are not accurate and flexible to new tag-sets and languages. In this regard, the statistical/rule-based hybrid method that can take advantages of both approaches is called for the robust and flexible POS disambiguation. We present one of such method, that is, a two-phase learning architecture for the hybrid statistical/rule-based POS disambiguation, especially for Korean. In this method, the statistical learning of morphological tagging is error-corrected by the rule-based learning of Brill [1992] style tagger. We also design the hierarchical and flexible Korean tag-set to cope with the multiple tagging applications, each of which requires different tag-set. Our experiments show that the two-phase learning method can overcome the undesirable features of solely HMM-based or solely rule-based tagging, especially for morphologically complex Korean.
dc.description10pages, latex, named.sty & named.bst, use psfig figures, submitted
dc.identifierhttps://arxiv.org/abs/cmp-lg/9504023
dc.identifierhttp://arxiv.org/abs/cmp-lg/9504023
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/147414
dc.subjectComputation and Language
dc.titleTAKTAG: Two-phase learning method for hybrid statistical/rule-based part-of-speech disambiguation
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