  Both 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.
