We present a statistical model of Japanese unknown words consisting of a set of length and spelling models classified by the character types that constitute a word. The point is quite simple: different character sets should be treated differently and the changes between character types are very important because Japanese script has both ideograms like Chinese  (kanji)  and phonograms like English  (katakana) . Both word segmentation accuracy and part of speech tagging accuracy are improved by the proposed model. The model can achieve 96.6% tagging accuracy if unknown words are correctly segmented.
['Masaaki Nagata']
A Part of Speech Estimation Method for Japanese Unknown Words using a Statistical Model of Morphology and Context