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Improving few-shot relation extraction through semantics-guided learning.

Hui Wu1, Yuting He2, Yidong Chen3

  • 1Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen, 361005, China; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005, China; Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University), Ministry of Culture and Tourism, Xiamen, 361005, China.

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Summary
This summary is machine-generated.

Semantics-Guided Learning (SemGL) improves few-shot relation extraction by enhancing instance and prototype representations. This method effectively utilizes relation information, boosting performance on challenging domain adaptation tasks.

Keywords:
Contrastive learningFew-shot relation extractionPrototype networkRelation graph learningRelation informationSemantics-guided learning

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Artificial Intelligence

Background:

  • Few-shot relation extraction (few-shot RE) identifies relationships between entities using limited data.
  • Existing prototype network methods enhance representations or use contrastive learning but struggle with outliers and class confusion.

Purpose of the Study:

  • To propose Semantics-Guided Learning (SemGL) for improved few-shot RE performance.
  • To enhance instance and prototype representations by effectively utilizing relation information.

Main Methods:

  • SemGL uses a prompt encoder for semantic representation enhancement via large language models.
  • Relation graph learning clusters instances using concept prototypes.
  • Instance-level and prototype-level contrastive learning are employed to refine feature discrimination.

Main Results:

  • SemGL demonstrates effectiveness and efficiency in few-shot RE.
  • The method shows promising results, particularly for domain adaptation challenges.
  • Experimental validation on two public datasets confirms SemGL's performance.

Conclusions:

  • SemGL offers a novel approach to few-shot relation extraction by integrating semantic guidance.
  • The method effectively addresses limitations of existing techniques, improving accuracy and robustness.
  • SemGL shows potential for advancing relation extraction in low-resource scenarios.