Approaches for semantic textual similarity

HAN Chengcheng; LI Lei; LIU Tingting; GAO Ming

doi:10.3969/j.issn.1000-5641.202091011

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HAN Chengcheng, LI Lei, LIU Tingting, GAO Ming. Approaches for semantic textual similarity[J]. Journal of East China Normal University (Natural Sciences), 2020, (5): 95-112. doi: 10.3969/j.issn.1000-5641.202091011

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HAN Chengcheng, LI Lei, LIU Tingting, GAO Ming. Approaches for semantic textual similarity[J]. Journal of East China Normal University (Natural Sciences), 2020, (5): 95-112. doi: 10.3969/j.issn.1000-5641.202091011

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Approaches for semantic textual similarity

doi: 10.3969/j.issn.1000-5641.202091011

School of Data Science and Engineering, East China Normal University, Shanghai　200062, China

Received Date: 2020-08-09
Available Online: 2020-09-24
Publish Date: 2020-09-24

Abstract

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.
- textual similarity,
- semantic similarity,
- natural language processing,
- knowledge base,
- deep learning

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References(87)

References

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