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BioCreative specifications
Information
Unique identifier | OMICS_13658 |
---|---|
Name | BioCreative |
Alternative name | Critical Assessment of Information Extraction systems in Biology |
Interface | Application programming interface |
Restrictions to use | None |
Computer skills | Advanced |
Version | 5.0 |
Stability | Stable |
Maintained | Yes |
Maintainer
- person_outline Cecilia Arighi
Publications for Critical Assessment of Information Extraction systems in Biology
Overview of the interactive task in BioCreative V
Overview of the gene ontology task at BioCreative IV
BioCreative III interactive task: an overview
Evaluation of text-mining systems for biology: overview of the Second BioCreative community challenge
BioCreative citations
(334)Deep learning meets ontologies: experiments to anchor the cardiovascular disease ontology in the biomedical literature
[…] The BioCreative (Critical Assessment of Information Extraction systems in Biology) challenge [] focuses on recognition of entities in text (i.e. NER) as well as relation extraction. For BioCreative II, Smith et al. [] […]
Textpresso Central: a customizable platform for searching, text mining, viewing, and curating biomedical literature
[…] spond to the needs of biocurators and the text mining community. Much effort has been devoted to understanding the critical needs of the biocuration workflow. Through community-wide endeavors such as BioCreative (Critical Assessment of Information Extraction in Biology), the biocuration and text mining communities have come together to determine the ways in which text mining tools can assist in th […]
Bio SimVerb and Bio SimLex: wide coverage evaluation sets of word similarity in biomedicine
[…] We assess our representation models using a NER task with four established corpora: the Anatomical Entity Mention corpus (AnatEM) [], the BioCreative II Gene Mention task corpus (BC2GM) [], the BioCreative IV Chemical and Drug NER corpus (BC4CHEMD) and the JNLPBA corpus (JNLPBA) [].The NER model follows the simple window-based feed-forw […]
Exploiting graph kernels for high performance biomedical relation extraction
[…] f our two relation extraction subsystems. The column “All Relations” represents the performance of the final relation extraction system over the full CDR test data, that corresponds to the subtask of BioCreative-V []. […]
Disease named entity recognition from biomedical literature using a novel convolutional neural network
[…] corpus and 2 h for the CDR corpus.We validated the effectiveness of MCNN by applying it to two corpora containing both mention-level and concept-level annotations: the NCBI Disease corpus [] and the BioCreative V Chemical Disease Relation task (CDR) corpus []. Overall statistics for each dataset are provided in Table . The NCBI Disease corpus consists of 793 Medline abstracts separated into train […]
Collaborative relation annotation and quality analysis in Markyt environment
[…] documents in TSV and BioC inline XML []. Moreover, the tool is able to import annotations following the formats BRAT standoff annotation [], BioC inline XML [], BioNLP standoff representation [] and BioCreative TSV []. Both documents and annotations are stored in the relational database supporting Markyt operations. Document contents are saved in HTML format with UTF-8 encoding which ensures mult […]
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