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Bidirectional matching and aggregation network for few-shot relation extraction.

Zhongcheng Wei1,2, Wenjie Guo1,2, Yunping Zhang1,2

  • 1School of Information and Electrical Engineering, Hebei University of Engineering, Handan, Hebei, China.

Peerj. Computer Science
|June 22, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a bidirectional matching and aggregation network (BMAN) for few-shot relation extraction. BMAN enhances model performance by considering data symmetry and employing data augmentation for improved generalization.

Keywords:
Few-shot learningKnowledge graphLong-tail distributionPrototypical networkRelation extraction

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Artificial Intelligence

Background:

  • Few-shot relation extraction addresses data imbalance in long-tail distributions by matching query and support instances.
  • Current methods often overlook the inherent symmetry within the data, focusing solely on unidirectional matching.
  • This limitation hinders performance, especially when training data exhibits symmetrical characteristics.

Purpose of the Study:

  • To propose a novel Bidirectional Matching and Aggregation Network (BMAN) to leverage data symmetry in few-shot relation extraction.
  • To enhance feature representation validation by seeking relational prototypes for query instances.
  • To mitigate overfitting issues through a data augmentation strategy tailored for instance relation classes.

Main Methods:

  • Development of the Bidirectional Matching and Aggregation Network (BMAN) model.
  • Implementation of a bidirectional matching process considering query-to-support and support-to-query relationships.
  • Introduction of a data augmentation technique to increase instance diversity while preserving relation class scope.

Main Results:

  • BMAN demonstrates superior performance, particularly on datasets with symmetrical training data.
  • The bidirectional approach effectively validates feature representations by comparing query instances with relational prototypes.
  • Experimental validation on FewRel and FewRel2.0 datasets confirms the model's effectiveness.

Conclusions:

  • The proposed BMAN effectively utilizes data symmetry for improved few-shot relation extraction.
  • Bidirectional matching and data augmentation are key components for enhancing model robustness and generalization.
  • The findings offer a significant advancement in handling long-tail distributions within relation extraction tasks.