For example, if " " (deep) appeared only once in training corpus as in a word " " (is deep), then we will not be able to use the information " " in a phrase " " (if it is deep) when we classify a character " " in " ."
target character position ↓ -3 -2 -1 0 1 2 3 ⟨s⟩ ⟨/s⟩ ( Though we planned to publish a big magazine that compares favorably with the one in that country, )  we carry out clustering on Kanji characters and add character class n-gramin feature sets.