No abstract

Generated topic label, an alternative representation of topics learned by topic model, is widely used to help the user interpret the topics more efficiently. A major challenge now is to label a discovered topic accurately in an objective way. This article introduces a novel graph-based ranking model (TLRank), to find a meaningful topic label with high Relevance, Coverage, and Discrimination. The model applies a specific strategy that suppresses or enhances the matrix transition probability according to the textual similarity between vertices (sentences) and the characteristics of vertices respectively. Moreover, to boost diversity and enhance performance, TLRank scores the candidate sentences and refrains redundancy of topic labels simultaneously in a single labeling process. In our experiments, the evaluation results showed that the TLRank model significantly and consistently outperformed the prevailing state-of-the-art and classic models in topic labeling task.INDEX TERMS Graph-based ranking, topic model, automatic labeling, topic label, labeling, LDA.

Cloud computing is low-cost, high-performance network application model, is gradually affecting study, work and life of people. This article introduces cloud computing and function of cloud computing, then analyzes the exiting problems of campus network resource management, the cloud computing technology and methods are applied in the construction of college information sharing platform, which can not only improve the utilization of college information resource and college information resources management, but also achieve efficient application and centralized management of college information resource with the help of efficient cloud computing capabilities and unlimited storage capacity.Index Terms-cloud computing, campus network, resource management.I.