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A Long-Tail Relation Extraction Model Based on Dependency Path and Relation Graph Embedding

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Web and Big Data (APWeb-WAIM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14332))

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Abstract

Distant supervision, a method for relation extraction, leverages knowledge base triples to label entities and relations in text, but this leads to noisy labels and long-tail problems. Among long-tail dependency structures, the hierarchy tree of relations is the most classical and has demonstrated great efficacy in information extraction. However, the hierarchical tree of relations presents a challenge in obtaining sufficient information representation in cases where there is no sibling node or parent node without sibling node. To address this challenge, the use of constraint graphs has been proposed, but such approaches neglect the hierarchical information in the relations. To overcome this limitation, we propose a model based on dependency paths and relational graph embeddings. The model utilizes two relational graph structures, the constraint graph and the relation hierarchy tree, for relation learning, with the aim of transferring the knowledge learned in the data-rich relation to the long-tail relation. Additionally, the model leverages the shortest dependency path between entity pairs to increase the discriminative power of entity pairs in different bags for multi-instance learning. Experimental results show that the model achieves an AUC of 54.3% on the NYT-10 dataset and 86.3% on Hit@15 (<100).

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Acknowledgements

This research was supported by the Dongbo Future Artificial Intelligence Research Institute Co., Ltd. Joint Laboratory (School Agreement No. 20223160C0026), Xiaozhi Deep Art Artificial Intelligence Research Institute Co., Ltd. Computational Art Joint Laboratory (School Agreement No. 20213160C0032), and Xiamen Yinjiang Smart City Joint Research Center (School Agreement No. 20213160C0029).

Author information

Authors and Affiliations

  1. School of Film, Xiamen University, Xiamen, 361005, China

    Yifan Li, Qingqiang Wu & Qingqi Hong

  2. School of Informatics, Xiamen University, Xiamen, 361005, China

    Yanxiang Zong, Wen Sun & Qingqiang Wu

  3. Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism, Xiamen University, Xiamen, China

    Qingqiang Wu

Authors
  1. Yifan Li
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  2. Yanxiang Zong
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  3. Wen Sun
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  4. Qingqiang Wu
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  5. Qingqi Hong
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Corresponding authors

Correspondence to Qingqiang Wu or Qingqi Hong .

Editor information

Editors and Affiliations

  1. Peng Cheng Laboratory, Shenzhen, China

    Xiangyu Song

  2. China University of Geosciences, Wuhan, China

    Ruyi Feng

  3. China University of Geosciences, Wuhan, China

    Yunliang Chen

  4. Deakin University, Burwood, VIC, Australia

    Jianxin Li

  5. University of Exeter, Exeter, UK

    Geyong Min

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Li, Y., Zong, Y., Sun, W., Wu, Q., Hong, Q. (2024). A Long-Tail Relation Extraction Model Based on Dependency Path and Relation Graph Embedding. In: Song, X., Feng, R., Chen, Y., Li, J., Min, G. (eds) Web and Big Data. APWeb-WAIM 2023. Lecture Notes in Computer Science, vol 14332. Springer, Singapore. https://doi.org/10.1007/978-981-97-2390-4_28

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  • DOI: https://doi.org/10.1007/978-981-97-2390-4_28

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2389-8

  • Online ISBN: 978-981-97-2390-4

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