An Entity Relation Extraction Method for Few-Shot Learning on the Food Health and Safety Domain.

Min Zuo, Baoyu Zhang, Qingchuan Zhang, Wenjing Yan, Dongmei Ai

Abstract read

In one paragraph

Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Min ZuoBeijing Technology and Business University, National Engineering Laboratory for Agri-product Quality Traceability, Beijing 100048, China.ORCID https://orcid.org/0000-0003-3336-2940

Baoyu ZhangBeijing Technology and Business University, National Engineering Laboratory for Agri-product Quality Traceability, Beijing 100048, China.ORCID https://orcid.org/0000-0002-6518-1573

Qingchuan ZhangBeijing Technology and Business University, National Engineering Laboratory for Agri-product Quality Traceability, Beijing 100048, China.ORCID https://orcid.org/0000-0001-9225-7660

Wenjing YanBeijing Technology and Business University, National Engineering Laboratory for Agri-product Quality Traceability, Beijing 100048, China.ORCID https://orcid.org/0000-0001-5746-2474

Dongmei AiBasic Experimental of Natural Science, University of Science and Technology Beijing, Beijing 100048, China.ORCID https://orcid.org/0000-0002-6935-6895

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, entity relation extraction has been a critical technique to help people analyze complex structured text data. However, there is no advanced research in food health and safety to help people analyze the complex concepts between food and human health and their relationships. This paper proposes an entity relation extraction method FHER for the few-shot learning in the food health and safety domain. For few-shot learning in the food health and safety domain, we propose three methods that effectively improve the performance of entity relationship extraction. The three methods are applied to the self-built data sets FH and MHD. The experimental results show that the method can effectively extract domain-related entities and their relations in a small sample size environment.

Indexed as

Food SafetyMachine LearningData AnalysisHumans

Identifiers

PMID35237307

DOI10.1155/2022/1879483

PMCPMC8885244

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

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.