Paper Info
Title
TAKTAG: Two-phase learning method for hybrid statistical/rule-based part-of-speech disambiguation
Abstract
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.
Year
Venue
Keywords
1995
Clinical Orthopaedics and Related Research
error correction,hidden markov model,rule based,part of speech
Field
DocType
Volume
Rule-based system,Computer science,Learning architecture,Speech recognition,Part of speech,Natural language processing,Artificial intelligence,Statistical learning,Hidden Markov model,Machine learning
Journal
cmp-lg/950
Citations 
PageRank 
References 
0
0.34
7
Authors
3
Name
Order
Citations
PageRank
Geunbae Lee14811.22
Jong-Hyeok Lee274097.88
Sanghyun Shin3122.88