Recherche Data Gouv

Dataset . 2016

Data sources: Recherche Data Gouv

https://dx.doi.org/10.57745/8w...

Dataset . 2022

Data sources: Datacite

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Dataset . 2022

Data sources: B2FIND

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SeeDev

Name: SeeDev
Keywords: Health and Life Sciences, Medicine, Health and Life Sciences, Agricultural Sciences, Computer and Information Science, Computer Science, Medicine, Life Sciences, Agriculture, Forestry, Horticulture

Research datakeyboard_double_arrow_right Dataset 01 Jan 2016Publisher:Recherche Data Gouv

Authors: Chaix, Estelle; Dubreucq, Bertrand; Fatihi, Abdelhak; Valsamou, Dialekti; Bossy, Robert; Ba, Mouhamadou; Deléger, Louise; +4 Authors

doi: 10.57745/8wgiq3

SeeDev

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Abstract

SeeDev is a set of article full-text excerpts manually annotated for training and evaluating Information Extraction systems. The documents were selected on the topic of plant seed development genetics. The annotations denote gene, proteins, plants, organs, and development stages, as well as relations between entities. SeeDev was proposed as a task in the BioNLP Shared Task 2016 and the BioNLP Open Shared Tasks 2019. It meets the BioNLP-OST standards of quality, originality and data formats. The tasks include binary and n-ary Relation Extraction.

Related Organizations

French National Centre for Scientific Research
France

Keywords

Health and Life Sciences, Medicine, Health and Life Sciences, Agricultural Sciences, Computer and Information Science, Computer Science, Medicine, Life Sciences, Agriculture, Forestry, Horticulture

1 Research products, page 1 of 1

YNU-junyi in BioNLP-OST 2019: Using CNN-LSTM Model with Embeddings for SeeDev Binary Event Extraction
2019IsAmongTopNSimilarDocuments

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average

SeeDev

SeeDev

1 Research products, page 1 of 1

YNU-junyi in BioNLP-OST 2019: Using CNN-LSTM Model with Embeddings for SeeDev Binary Event Extraction