ArticleBioinformatics (Oxford, England)2014
Anatomical entity mention recognition at literature scale.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
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- Explanatory argumentation in natural language for correct and incorrect medical diagnoses.Journal of biomedical semantics · 2024Article
- Europe PMC annotated full-text corpus for gene/proteins, diseases and organisms.Scientific data · 2023Article
- Automatic classification of experimental models in biomedical literature to support searching for alternative methods to animal experiments.Journal of biomedical semantics · 2023Article
- S1000: a better taxonomic name corpus for biomedical information extraction.Bioinformatics (Oxford, England) · 2023Article
- Consistency enhancement of model prediction on document-level named entity recognition.Bioinformatics (Oxford, England) · 2023Article
- AIONER: all-in-one scheme-based biomedical named entity recognition using deep learning.Bioinformatics (Oxford, England) · 2023Article
- A survey on clinical natural language processing in the United Kingdom from 2007 to 2022.NPJ digital medicine · 2022Review
- NLIMED: Natural Language Interface for Model Entity Discovery in Biosimulation Model Repositories.Frontiers in physiology · 2022Article
- Knowledge Graphs of Kawasaki Disease.Health information science and systems · 2021Article
- The Unified Medical Language System at 30 Years and How It Is Used and Published: Systematic Review and Content Analysis.JMIR medical informatics · 2021Review
- Toward systematic review automation: a practical guide to using machine learning tools in research synthesis.Systematic reviews · 2019Article
- Thalia: semantic search engine for biomedical abstracts.Bioinformatics (Oxford, England) · 2019Article
- Automatic Normalization of Anatomical Phrases in Radiology Reports Using Unsupervised Learning.Journal of digital imaging · 2019Article
- A reference set of curated biomedical data and metadata from clinical case reports.Scientific data · 2018Article
- Exploiting and assessing multi-source data for supervised biomedical named entity recognition.Bioinformatics (Oxford, England) · 2018Article
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2 authors.
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Abstract
motivationAnatomical entities ranging from subcellular structures to organ systems are central to biomedical science, and mentions of these entities are essential to understanding the scientific literature. Despite extensive efforts to automatically analyze various aspects of biomedical text, there have been only few studies focusing on anatomical entities, and no dedicated methods for learning to automatically recognize anatomical entity mentions in free-form text have been introduced.
resultsWe present AnatomyTagger, a machine learning-based system for anatomical entity mention recognition. The system incorporates a broad array of approaches proposed to benefit tagging, including the use of Unified Medical Language System (UMLS)- and Open Biomedical Ontologies (OBO)-based lexical resources, word representations induced from unlabeled text, statistical truecasing and non-local features. We train and evaluate the system on a newly introduced corpus that substantially extends on previously available resources, and apply the resulting tagger to automatically annotate the entire open access scientific domain literature. The resulting analyses have been applied to extend services provided by the Europe PubMed Central literature database. AVAILABILITY AND IMPLEMENTATION: All tools and resources introduced in this work are available from http://nactem.ac.uk/anatomytagger. CONTACT: sophia.ananiadou@manchester.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.