Abstract: To address the problems of information loss in traditional Japanese text representation and the difficulty in processing high-dimensional sparse vectors,we study Japanese text word extraction and clustering methods.Firstly,the words are extracted using the improved atomic-word-step method based on Japanese linguistic characteristics.The Multi-attribute Fusion Weight (MFW) of the words is calculated combining their statistical features,positions,word lengths and semantic features so as to obtain a set of text feature words for retaining text information while reducing feature dimensionality.Then,Japanese texts are represented as the BERT-weighted MFWs of feature words,which is fused into the deep embedding model framework improved by the K-means++ algorithm to realize the clustering of Japanese texts.Experimental results on two Japanese text datasets with different topics show that the approach proposed in this paper improves both the NMI and Purity index values by more than 5% compared with the existing methods,which demonstrates a good clustering performance.

Key words: Japanese text mining, Word extraction, Text representation, Text clustering, Deep clustering