Document representation is a key problem in document analysis and processing tasks, such as document classification, clustering and information retrieval. Espe-cially for unstructured text data, the use of a suitable document representation method would affect the perfor-mance of the subsequent algorithms for applications and research. In this paper, we propose a novel document repre- sentation method called the conditional co-occurrence degree
matrix document representation method (CCODM), which is based on word co-occurrence. CCODM not only considers the co-occurrence of terms but also considers the conditional dependencies of terms in a specific context, which leads to more available and useful structural and semantic informa-
tion being retained from the original documents. Extensive experimental classification results with different supervised and unsupervised feature selection methods show that the proposed method, CCODM, achieves better performance than the vector space model, latent Dirichlet allocation, the
general co-occurrence matrix representation method and the document embedding method.

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