Semantic Document Classification based on Strategies of Semantic Similarity Computation and Correlation Analysis

摘要

Document (text) classification is a common method in e-business, facilitating users in the taskssuch as document collection, analysis, categorization and storage. Semantic analysis can helpto improve the performance of document classification. Though having been considered whendesigning previous methods for automatic document classification, more focus should be givento semantics with the increase number of content-rich electronic documents, forum posts orblogs online, which can reduce human workload by a great margin. This paper proposes anovel semantic document classification approach aiming to resolve two types of semanticproblems: (1) polysemy problem, by using a novel semantic similarity computing strategy (SSC)and (2) synonym problem, by proposing a novel strong correlation analysis method (SCM).Experiments show that our strategies can help to improve the performance of the baselinemethods.