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Empowering Chinese Hypernym-Hyponym Relation Extraction Leveraging Entity Description and Attribute Information

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Web Information Systems and Applications (WISA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14094))

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Abstract

Hypernym-hyponym relations play a crucial role in various entity-based natural language processing applications. Most previous hypernym-hyponym relation extraction approaches used pattern-based, encyclopedia-based, and clustering-based methods; however, the performance of these approaches in Chinese texts is not ideal. In this paper, we introduce an entity description and attribute information-based approach for extracting Chinese hypernym-hyponym relations. First, we mine entity descriptions and attribute information from a Chinese encyclopedia, followed by applying a filtering strategy to gather candidate entities from plain Chinese texts. Following this, we develop a neural network extraction model that integrates entity descriptions and attribute information to identify hypernym-hyponym relations among the collected Chinese candidate entities. In this model, bidirectional gated recurrent units (Bi-GRU) are utilized to extract Chinese hypernym-hyponym semantic information from entity descriptions, while an attention model captures Chinese hypernym-hyponym semantic information from attribute information. We implement the proposed approach on four real-world Chinese datasets, demonstrating its practicality and superiority to compared approaches concerning evaluation metrics. This paper contributes to enhancing the accuracy of hypernym-hyponym relation extraction in Chinese texts.

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Notes

  1. 1.

    https://baike.baidu.com/.

  2. 2.

    http://www.sogou.com/labs/resource/ca.php.

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Acknowledgement

This research was funded by the University Natural Science Research Projects of Anhui Province (Grant Nos. 2022AH050972, KJ2020A0361, and KJ2021A0516). This research was jointly funded by the Key Technologies R &D Program of the Anhui Province of China (Grant No. 202004a05020013), the Key Project of Collaborative Innovation Fund of Jiujiang District and Anhui Polytechnic University (Grant No. 2021cyxta1), and the Excellent Young Talents Fund Program of Higher Education Institutions of Anhui Province (Grant No. gxyq2020031).

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Authors and Affiliations

  1. School of Computer and Information, Anhui Polytechnic University, Wuhu, China

    Senyan Zhao, ChengZhen Yu, Subin Huang & Chao Kong

  2. School of Artificial Intelligence, Anhui Polytechnic University, Wuhu, China

    Buyun Wang

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  1. Senyan Zhao
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  2. ChengZhen Yu
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  3. Subin Huang
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  5. Chao Kong
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Corresponding author

Correspondence to Subin Huang .

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Editors and Affiliations

  1. Nanjing University of Science and Technology, Nanjing, China

    Long Yuan

  2. Guangzhou University, Guangzhou, China

    Shiyu Yang

  3. Huazhong University of Science and Technology, Wuhan, China

    Ruixuan Li

  4. University of Amsterdam, Amsterdam, The Netherlands

    Evangelos Kanoulas

  5. National University of Defense Technology, Changsha, China

    Xiang Zhao

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Zhao, S., Yu, C., Huang, S., Wang, B., Kong, C. (2023). Empowering Chinese Hypernym-Hyponym Relation Extraction Leveraging Entity Description and Attribute Information. In: Yuan, L., Yang, S., Li, R., Kanoulas, E., Zhao, X. (eds) Web Information Systems and Applications. WISA 2023. Lecture Notes in Computer Science, vol 14094. Springer, Singapore. https://doi.org/10.1007/978-981-99-6222-8_8

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  • DOI: https://doi.org/10.1007/978-981-99-6222-8_8

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