ArticleJournal of biomedical semantics2017
Entity recognition in the biomedical domain using a hybrid approach.
Article in Journal of biomedical semantics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- From literature to biodiversity data: mining arthropod organismal traits with machine learning.Biodiversity data journal · 2025Article
- An Accurate and Efficient Approach to Knowledge Extraction from Scientific Publications Using Structured Ontology Models, Graph Neural Networks, and Large Language Models.International journal of molecular sciences · 2024Article
- Automated annotation of scientific texts for ML-based keyphrase extraction and validation.Database : the journal of biological databases and curation · 2024Article
- A Gated Recurrent Unit based architecture for recognizing ontology concepts from biological literature.BioData mining · 2022Article
- A review of research on neuromarketing using content analysis: key approaches and new avenues.Cognitive neurodynamics · 2021Article
- Improving classification of low-resource COVID-19 literature by using Named Entity Recognition.Genomics & informatics · 2021Article
- Editor's introduction to the special section on the 7th Biomedical Linked Annotation Hackathon (BLAH7).Genomics & informatics · 2021Article
- Learning adaptive representations for entity recognition in the biomedical domain.Journal of biomedical semantics · 2021Article
- CoCoScore: context-aware co-occurrence scoring for text mining applications using distant supervision.Bioinformatics (Oxford, England) · 2020Article
- OGER++: hybrid multi-type entity recognition.Journal of cheminformatics · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundThis article describes a high-recall, high-precision approach for the extraction of biomedical entities from scientific articles.
methodThe approach uses a two-stage pipeline, combining a dictionary-based entity recognizer with a machine-learning classifier. First, the OGER entity recognizer, which has a bias towards high recall, annotates the terms that appear in selected domain ontologies. Subsequently, the Distiller framework uses this information as a feature for a machine learning algorithm to select the relevant entities only. For this step, we compare two different supervised machine-learning algorithms: Conditional Random Fields and Neural Networks.
resultsIn an in-domain evaluation using the CRAFT corpus, we test the performance of the combined systems when recognizing chemicals, cell types, cellular components, biological processes, molecular functions, organisms, proteins, and biological sequences. Our best system combines dictionary-based candidate generation with Neural-Network-based filtering. It achieves an overall precision of 86% at a recall of 60% on the named entity recognition task, and a precision of 51% at a recall of 49% on the concept recognition task.
conclusionThese results are to our knowledge the best reported so far in this particular task.
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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.