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Semantic Dependency Analysis of Special Sentence Patterns in Ancient Chinese

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Chinese Lexical Semantics (CLSW 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13495))

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Abstract

With the development of natural language processing, automatic semantic analysis has attracted more and more attention. This research delineates the semantic dependency labeling rules of several kinds of special sentence patterns in ancient Chinese. And we compare the result of manual annotation with automatic semantic parser and put forward improvement methods to those poor results. This research can help to improve the semantic understanding of ancient Chinese, the translation of ancient Chinese and the perfomance of parser to a certain extent.

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References

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Acknowledgments

This research project is supported by the National Natural Science Foundation of China (61872402), the Humanities and Social Science Project of the Ministry of Education (17YJAZH068), Science Foundation of Beijing Language and Culture University (supported by “the Fundamental Research Funds for the Central Universities”) (18ZDJ03).

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Authors and Affiliations

  1. College of Information Sciences, Beijing Language and Culture University, Beijing, China

    Xuan Chen & Yanqiu Shao

  2. National Language Resources Monitoring and Research Center (CNLR) Print Media Language Branch, Beijing, China

    Xuan Chen & Yanqiu Shao

Authors
  1. Xuan Chen
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  2. Yanqiu Shao
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Corresponding author

Correspondence to Yanqiu Shao .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Qi Su

  2. Minjiang University, Fuzhou, China

    Ge Xu

  3. Minjiang University, Fuzhou, China

    Xiaoyan Yang

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Chen, X., Shao, Y. (2023). Semantic Dependency Analysis of Special Sentence Patterns in Ancient Chinese. In: Su, Q., Xu, G., Yang, X. (eds) Chinese Lexical Semantics. CLSW 2022. Lecture Notes in Computer Science(), vol 13495. Springer, Cham. https://doi.org/10.1007/978-3-031-28953-8_26

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  • DOI: https://doi.org/10.1007/978-3-031-28953-8_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28952-1

  • Online ISBN: 978-3-031-28953-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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