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

17 Pages Posted: 19 Jan 2021 Publication Status: Accepted

See all articles by Shuo Yang

Shuo Yang

Guangzhou University - School of Computer Science and Cyber Engineering

Ran Wei

Department of Computer Science, University of California

Hengliang Tan

School of Computer Science and Cyber Engineering, Guangzhou University

Jiao Du

School of Computer Science and Cyber Engineering, Guangzhou University

Abstract

Document (text) classification is a common method in e-business, facilitating users in the tasks such as document collection, analysis, categorization and storage. Semantic analysis can help to improve the performance of document classification. Though having been considered when designing previous methods for automatic document classification, more focus should be given to semantics with the increase number of content-rich electronic documents, forum posts or blogs online, which can reduce human workload by a great margin. This paper proposes a novel semantic document classification approach aiming to resolve two types of semantic problems: (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 baseline methods.

Keywords: semantic document classification, semantic similarity, semantic embedding, correlation analysis, machine learning

Suggested Citation

Yang, Shuo and Wei, Ran and Tan, Hengliang and Du, Jiao, Semantic Document Classification Based on Strategies of Semantic Similarity Computation and Correlation Analysis. Available at SSRN: https://ssrn.com/abstract=3769520 or http://dx.doi.org/10.2139/ssrn.3769520

Shuo Yang (Contact Author)

Guangzhou University - School of Computer Science and Cyber Engineering

Guangzhou
China

Ran Wei

Department of Computer Science, University of California

Hengliang Tan

School of Computer Science and Cyber Engineering, Guangzhou University ( email )

Jiao Du

School of Computer Science and Cyber Engineering, Guangzhou University ( email )

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