Clustering of News Topics Integrating the Relationship among News Elements

To make full use of news document structure and the relation among different news documents, a news topic clustering method is proposed of using the relation among document elements. First, the word characteristic weight was calculated by the TF-IDF method based on word frequency statistics to generate document space vector and news document similarity was calculated using text similarity measurement algorithm to obtain the initial news document similarity matrix. Then, the initial similarity matrix was modified with the relation among different news elements as semi-supervised constraint information, the clustering of news documents was realized using Affinity Propagation algorithm, and news topics were extracted from news clusters. As a result, the construction of news topic model was finished. At last, the contrast experiments were performed on manually-annotated news corpus. The results show that the Affinity Propagation clustering methods integrating the relation among document elements can achieve a better effect than those without constraint information.