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A Comprehensive Overview of CFN From a Commonsense Perspective

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Abstract

Chinese FrameNet (CFN) is a scenario commonsense knowledge base (CKB) that plays an important role in research on Chinese language understanding. It is based on the theory of frame semantics and English FrameNet (FN). The CFN knowledge base contains a wealth of scenario commonsense knowledge, including frames, frame elements, and frame relations, as well as annotated instances with rich scenario-related labels on Chinese sentences and discourses. In this paper, we conduct a comprehensive overview of CFN from a commonsense perspective, covering topics such as scenario commonsense representation, CFN resources, and its applications. We also summarize recent breakthroughs and identify future research directions. First, we introduce the concept of scenario commonsense, including its definitions, examples, and representation methods, with a focus on the relationship between scenario commonsense and the frame concept in CFN. In addition, we provide a comprehensive overview of CFN resources and their applications, highlighting the newly proposed frame-based discourse representation and a human-machine collaboration framework for expanding the CFN corpus. Furthermore, we explore emerging topics such as expanding the CFN resource, improving the interpretability of machine reading comprehension, and using scenario CKBs for text generation.

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Acknowledgements

We thank the reviewers for their helpful comments and suggestions. This work was supported by National Natural Science Foundation of China (Nos. 61936012, 62272285 and 61906111).

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  1. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China

    Ru Li, Yunxiao Zhao, Zhiqiang Wang, Xuefeng Su, Shaoru Guo, Yong Guan, Xiaoqi Han & Hongyan Zhao

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  1. Ru Li
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Colored figures are available in the online version at https://link.springer.com/journal/11633

Ru Li received the Ph.D. degree in computer application technology from Shanxi University, China in 2012. She is currently a professor with Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, China. She has published numerous papers in important academic journals and conferences, such as IEEE TKDE, ACL, IJCAI, EMNLP, COLING, NLPCC, etc.

Her research interests include natural language processing, text semantics analysis and machine learning.

Yunxiao Zhao received the B.Sc. degree in computer and information technology from Shanxi University, China in 2020. He is currently a Ph.D. degree candidate in computer science and technology at School of Computer and Information Technology, Shanxi University, China.

His research interests include natural language processing and text data mining.

Zhiqiang Wang received the Ph.D. degree in computer application technology from Shanxi University, China in 2018. He is currently an associate professor with School of Computer and Information Technology, Shanxi University, China. He received the Best Doctoral Dissertation of Chinese Information Processing Society of China (CIPS) in 2019. He has been serving as a reviewer for many academic journals and conferences such as IEEE Transactions on Neural Networks and Learning Systems, Neural Networks, Neurocomputing, ACL, KDD, COLING, and EMNLP.

His research interests include natural language processing, network big data mining and machine learning.

Xuefeng Su received the M.Sc. degree in computer application technology from Taiyuan University of Technology, China in 2007. He is an associated professor at the Shanxi Vocational University of Engineering Science and Technology, China and also a Ph. D. degree candidate in computer science and technology at the Shanxi University, China. Recently, he has published more than 10 papers in journals and conferences of NLP area, such as ACL, NLPCC, TALLIP, etc.

His research interests include data mining and semantic analysis on text.

Shaoru Guo received the Ph. D. degree in computer science and technology from Shanxi University, China in 2021. She is currently a postdoctoral researcher in Institute of Automation, Chinese Academy of Sciences, China.

Her research interests include machine reading comprehension and information extraction.

Yong Guan received the Ph.D. degree in computer science and technology from Shanxi University, China in 2022. He is currently a postdoctoral researcher in Department of Computer Science and Technology, Tsinghua University, China.

His research interests include text generation and information extraction.

Xiaoqi Han received the B.Sc. degree in computer science and technology from Hefei University of Technology, China 2019. He is currently a Ph.D. degree candidate in computer science and technology at Shanxi University, China. During his Ph.D. study, he took part in Research Chinese FrameNet Computing Based on Language Cognitive Mechanism Project, mainly responsible for the semantic analysis.

His research interests include natural language processing and semantic parsing.

Hongyan Zhao received the Ph.D. degree in computer application technology from Shanxi University, China in 2021. She is currently an associate professor with School of Computer Science and Technology, Taiyuan University of Science and Technology, China.

Her research interests include natural language processing, inteUigent data mining and information extraction.

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Li, R., Zhao, Y., Wang, Z. et al. A Comprehensive Overview of CFN From a Commonsense Perspective. Mach. Intell. Res. 21, 239–256 (2024). https://doi.org/10.1007/s11633-023-1450-8

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