In the present paper, we investigate the dynamical features of the rule sequence at each time step which realize the description of digital sound data. From previous research, we have performed the description with the limited number of 256 rules of three-rules sets of one dimensional cellular automata with two states and three neighbors referred as to 1-2-3 CA hereafter for various digital sounds and Huffman coding, and have successfully achieved a fully compressed description of the target sound data without reproducing the error. In order to investigate the dynamical features of the rule sequences at each time step, we perform numerical Wolfram's classification method. From computer experiments, in Pronounced word data, all the rule sequences are composed with Wolfram's class1, 2 and 4, whereas in musical data, all the rule sequences are composed with Wolfram's class2 and 4. Thus, we have succeed to classify the rule sequences among the genres of data.

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Word vectors are applied various tasks in natural language processing. However, the potentiality of the word vectors of Japanese has not been discussed as much as those of English. Therefore, the purpose of this paper is to classify the genre of modern Japanese literary works using characteristic features evaluated by the word vectors. The accuracy of the classification between novels and poetic works was 95%, and the one between novels and essays was 90%. The word vectors are applicable in the genre classification problem in modern Japanese literary works.

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