Journal of East China Normal University(Natural Science) ›› 2020, Vol. 2020 ›› Issue (5): 95-112.doi: 10.3969/j.issn.1000-5641.202091011

• Semantic Extraction from Data • Previous Articles     Next Articles

Approaches for semantic textual similarity

HAN Chengcheng, LI Lei, LIU Tingting, GAO Ming   

  1. School of Data Science and Engineering, East China Normal University, Shanghai 200062, China
  • Received:2020-08-09 Published:2020-09-24

Abstract: This paper summarizes the latest research progress on semantic textual similarity calculation methods, including string-based, statistics-based, knowledge-based, and deep-learning-based methods. For each method, the paper reviews not only typical models and approaches, but also discusses the respective advantages and disadvantages of each routine; the paper also explores public datasets and evaluation metrics commonly used. Finally, we put forward several possible directions for future research in the field of semantic textual similarity.

Key words: textual similarity, semantic similarity, natural language processing, knowledge base, deep learning

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