ArticleJournal of cheminformatics2019
OGER++: hybrid multi-type entity recognition.
Article in Journal of cheminformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Information extraction from green channel textual records on expressways using hybrid deep learning.Scientific reports · 2024Article
- Automated annotation of scientific texts for ML-based keyphrase extraction and validation.Database : the journal of biological databases and curation · 2024Article
- Sample Size Considerations for Fine-Tuning Large Language Models for Named Entity Recognition Tasks: Methodological Study.JMIR AI · 2024Article
- PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies.Genome medicine · 2024Article
- Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES): a method for populating knowledge bases using zero-shot learning.Bioinformatics (Oxford, England) · 2024Article
- Cancer-Alterome: a literature-mined resource for regulatory events caused by genetic alterations in cancer.Scientific data · 2024Article
- Europe PMC annotated full-text corpus for gene/proteins, diseases and organisms.Scientific data · 2023Article
- An annotated dataset for extracting gene-melanoma relations from scientific literature.Journal of biomedical semantics · 2022Article
- Concept recognition as a machine translation problem.BMC bioinformatics · 2021Article
- OnTheFlyNAR genomics and bioinformatics · 2021Article
- Context-aware multi-token concept recognition of biological entities.BMC bioinformatics · 2021Article
- LitCovid-AGAC: cellular and molecular level annotation data set based on COVID-19.Genomics & informatics · 2021Article
- From Reductionism to Reintegration: Solving society's most pressing problems requires building bridges between data types across the life sciences.PLoS biology · 2021Article
- Models and Processes to Extract Drug-like Molecules From Natural Language Text.Frontiers in molecular biosciences · 2021Article
- Zinc against COVID-19? Symptom surveillance and deficiency risk groups.PLoS neglected tropical diseases · 2021Review
- Named Entity Recognition and Relation Detection for Biomedical Information Extraction.Frontiers in cell and developmental biology · 2020Review
- Next generation community assessment of biomedical entity recognition web servers: metrics, performance, interoperability aspects of BeCalm.Journal of cheminformatics · 2019Article
- Improving spaCy dependency annotation and PoS tagging web service using independent NER services.Genomics & informatics · 2019Article
- Towards cross-platform interoperability for machine-assisted text annotation.Genomics & informatics · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundWe present a text-mining tool for recognizing biomedical entities in scientific literature. OGER++ is a hybrid system for named entity recognition and concept recognition (linking), which combines a dictionary-based annotator with a corpus-based disambiguation component. The annotator uses an efficient look-up strategy combined with a normalization method for matching spelling variants. The disambiguation classifier is implemented as a feed-forward neural network which acts as a postfilter to the previous step.
resultsWe evaluated the system in terms of processing speed and annotation quality. In the speed benchmarks, the OGER++ web service processes 9.7 abstracts or 0.9 full-text documents per second. On the CRAFT corpus, we achieved 71.4% and 56.7% F1 for named entity recognition and concept recognition, respectively.
conclusionsCombining knowledge-based and data-driven components allows creating a system with competitive performance in biomedical text mining.
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Registered trials
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 Socratic graph.