ArticleDatabase : the journal of biological databases and curation2016
BioCreative V track 4: a shared task for the extraction of causal network information using the Biological Expression Language.
Article in Database : the journal of biological databases and curation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- BiodivNERE: Gold standard corpora for named entity recognition and relation extraction in the biodiversity domain.Biodiversity data journal · 2022Article
- Extraction of causal relations based on SBEL and BERT model.Database : the journal of biological databases and curation · 2021Article
- Broad-coverage biomedical relation extraction with SemRep.BMC bioinformatics · 2020Article
- Hierarchical sequence labeling for extracting BEL statements from biomedical literature.BMC medical informatics and decision making · 2019Article
- Combining relation extraction with function detection for BEL statement extraction.Database : the journal of biological databases and curation · 2019Article
- The extraction of complex relationships and their conversion to biological expression language (BEL) overview of the BioCreative VI (2017) BEL track.Database : the journal of biological databases and curation · 2019Article
- Re-curation and rational enrichment of knowledge graphs in Biological Expression Language.Database : the journal of biological databases and curation · 2019Article
- Improving precision in concept normalization.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2018Article
- BELMiner: adapting a rule-based relation extraction system to extract biological expression language statements from bio-medical literature evidence sentences.Database : the journal of biological databases and curation · 2017Article
- The BioC-BioGRID corpus: full text articles annotated for curation of protein-protein and genetic interactions.Database : the journal of biological databases and curation · 2017Article
- Training and evaluation corpora for the extraction of causal relationships encoded in biological expression language (BEL).Database : the journal of biological databases and curation · 2016Article
- Coreference resolution improves extraction of Biological Expression Language statements from texts.Database : the journal of biological databases and curation · 2016Article
- BELTracker: evidence sentence retrieval for BEL statements.Database : the journal of biological databases and curation · 2016Article
- The BEL information extraction workflow (BELIEF): evaluation in the BioCreative V BEL and IAT track.Database : the journal of biological databases and curation · 2016Article
- BelSmile: a biomedical semantic role labeling approach for extracting biological expression language from text.Database : the journal of biological databases and curation · 2016Article
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7 authors.
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Abstract
Automatic extraction of biological network information is one of the most desired and most complex tasks in biological and medical text mining. Track 4 at BioCreative V attempts to approach this complexity using fragments of large-scale manually curated biological networks, represented in Biological Expression Language (BEL), as training and test data. BEL is an advanced knowledge representation format which has been designed to be both human readable and machine processable. The specific goal of track 4 was to evaluate text mining systems capable of automatically constructing BEL statements from given evidence text, and of retrieving evidence text for given BEL statements. Given the complexity of the task, we designed an evaluation methodology which gives credit to partially correct statements. We identified various levels of information expressed by BEL statements, such as entities, functions, relations, and introduced an evaluation framework which rewards systems capable of delivering useful BEL fragments at each of these levels. The aim of this evaluation method is to help identify the characteristics of the systems which, if combined, would be most useful for achieving the overall goal of automatically constructing causal biological networks from text.
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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.