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Sememe knowledge computation: a review of recent advances in application and expansion of sememe knowledge bases

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Abstract

A sememe is defined as the minimum semantic unit of languages in linguistics. Sememe knowledge bases are built by manually annotating sememes for words and phrases. HowNet is the most well-known sememe knowledge base. It has been extensively utilized in many natural language processing tasks in the era of statistical natural language processing and proven to be effective and helpful to understanding and using languages. In the era of deep learning, although data are thought to be of vital importance, there are some studies working on incorporating sememe knowledge bases like HowNet into neural network models to enhance system performance. Some successful attempts have been made in the tasks including word representation learning, language modeling, semantic composition, etc. In addition, considering the high cost of manual annotation and update for sememe knowledge bases, some work has tried to use machine learning methods to automatically predict sememes for words and phrases to expand sememe knowledge bases. Besides, some studies try to extend HowNet to other languages by automatically predicting sememes for words and phrases in a new language. In this paper, we summarize recent studies on application and expansion of sememe knowledge bases and point out some future directions of research on sememes.

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (2018YFB1004503) and the National Natural Science Foundation of China (NSFC Grant Nos. 61732008, 61532010). We also thank the anonymous reviewers for their comments.

Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China

    Fanchao Qi, Yuan Zang, Zhiyuan Liu & Maosong Sun

  2. Institute for Artificial Intelligence, Tsinghua University, Beijing, 100084, China

    Fanchao Qi, Yuan Zang, Zhiyuan Liu & Maosong Sun

  3. Beijing National Research Center for Information Science and Technology, Beijing, 100084, China

    Fanchao Qi, Yuan Zang, Zhiyuan Liu & Maosong Sun

  4. Search Product Center, WeChat Search Application Department, Tencent, Beijing, 100080, China

    Ruobing Xie

  5. Beijing Academy of Artificial Intelligence, Beijing, 100191, China

    Zhiyuan Liu & Maosong Sun

Authors
  1. Fanchao Qi
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  2. Ruobing Xie
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  3. Yuan Zang
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  4. Zhiyuan Liu
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  5. Maosong Sun
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Corresponding author

Correspondence to Zhiyuan Liu.

Additional information

Fanchao Qi is a PhD student of the Department of Computer Science and Technology, Tsinghua University, China. He got his BEng degree in 2017 from the Department of Electronic Engineering, Tsinghua University, China. His research interests include natural language processing and computational semantics. He has published papers in international conferences including AAAI, ACL and EMNLP.

Ruobing Xie is a researcher of WeChat, Ten-cent. He got his BEng degree in 2014 and his master degree in 2017 from the Department of Computer Science and Technology, Tsinghua University, China. His research interests are natural language processing and recommender system. He has published over 15 papers in international journals and conferences including IJCAI, AAAI, ACL and EMNLP.

Yuan Zang is an undergraduate student of the Department of Computer Science and Technology, Tsinghua University, China. His research interests lie in natural language processing and adversarial learning. He has published papers in ACL.

Zhiyuan Liu is an associate professor of the Department of Computer Science and Technology, Tsinghua University, China. He got his BEng degree in 2006 and his PhD in 2011 from the Department of Computer Science and Technology, Tsinghua University, China. His research interests are natural language processing and social computation. He has published over 40 papers in international journals and conferences including ACM Transactions, IJCAI, AAAI, ACL and EMNLP.

Maosong Sun is a professor of the Department of Computer Science and Technology, Tsinghua University, China. He got his BEng degree in 1986 and MEng degree in 1988 from Department of Computer Science and Technology, Tsinghua University, and got his PhD degree in 2004 from Department of Chinese, Translation, and Linguistics, City University of Hong Kong, China. His research interests include natural language processing, Chinese computing, Web intelligence, and computational social sciences. He has published over 150 papers in academic journals and international conferences in the above fields. He serves as the council member of China Computer Federation, the director of Massive Online Education Research Center of Tsinghua University, and the Editor-in-Chief of the Journal of Chinese Information Processing.

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Qi, F., Xie, R., Zang, Y. et al. Sememe knowledge computation: a review of recent advances in application and expansion of sememe knowledge bases. Front. Comput. Sci. 15, 155327 (2021). https://doi.org/10.1007/s11704-020-0002-4

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