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Chinese characters have a complex and hierarchical graphical structurecarrying both semantic and phonetic information. We use this structure toenhance the text model and obtain better results in standard NLP operations.First of all, to tackle the problem of graphical variation we defineallographic classes of characters. Next, the relation of inclusion of asubcharacter in a characters, provides us with a directed graph of allographicclasses. We provide this graph with two weights: semanticity (semantic relationbetween subcharacter and character) and phoneticity (phonetic relation) andcalculate "most semantic subcharacter paths" for each character. Finally,adding the information contained in these paths to unigrams we claim toincrease the efficiency of text mining methods. We evaluate our method on atext classification task on two corpora (Chinese and Japanese) of a total of 18million characters and get an improvement of 3% on an already high baseline of89.6% precision, obtained by a linear SVM classifier. Other possibleapplications and perspectives of the system are discussed.

New Perspectives in Sinographic Language Processing Through the Use of Character Structure