Evidence map›Paper›PMID 37346532›Full record

ArticlePeerJ. Computer science2023

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

Zhongcheng Wei, Wenjie Guo, Yunping Zhang, Jieying Zhang, Jijun Zhao

Abstract read
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Article in PeerJ. Computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Zhongcheng WeiSchool of Information and Electrical Engineering, Hebei University of Engineering, Handan, Hebei, China.
Wenjie GuoSchool of Information and Electrical Engineering, Hebei University of Engineering, Handan, Hebei, China.
Yunping ZhangSchool of Information and Electrical Engineering, Hebei University of Engineering, Handan, Hebei, China.
Jieying ZhangSchool of Information and Electrical Engineering, Hebei University of Engineering, Handan, Hebei, China.
Jijun ZhaoSchool of Information and Electrical Engineering, Hebei University of Engineering, Handan, Hebei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Few-shot relation extraction is used to solve the problem of long tail distribution of data by matching between query instances and support instances. Existing methods focus only on the single direction process of matching, ignoring the symmetry of the data in the process. To address this issue, we propose the bidirectional matching and aggregation network (BMAN), which is particularly powerful when the training data is symmetrical. This model not only tries to extract relations for query instances, but also seeks relational prototypes about the query instances to validate the feature representation of the support set. Moreover, to avoid overfitting in bidirectional matching, the data enhancement method was designed to scale up the number of instances while maintaining the scope of the instance relation class. Extensive experiments on FewRel and FewRel2.0 public datasets are conducted and evaluate the effectiveness of BMAN.

Indexed as

Few-shot learningKnowledge graphLong-tail distributionPrototypical networkRelation extraction

Identifiers

PMID37346532
PMCPMC10280494

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.