ArticlePloS one2013
The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 1 of them a synthesis that pooled it.
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Who cites it
90 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Microbial named entity recognition and normalisation for AI-assisted literature review and meta-analysis.Bioinformatics (Oxford, England) · 2026Pooled it
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- Improving biomedical entity linking with generative relevance feedback.Bioinformatics (Oxford, England) · 2026Article
- Utilizing large language models to construct a dataset of Württemberg's 19th-century fauna from historical records.PloS one · 2026Article
- Clinical insights: A comprehensive review of language models in medicine.PLOS digital health · 2025Review
- LitSumm: large language models for literature summarization of noncoding RNAs.Database : the journal of biological databases and curation · 2025Article
- GRU-SCANET: unleashing the power of GRU-based sinusoidal capture network for precision-driven named entity recognition.Bioinformatics advances · 2025Article
- Darling (v2.0): Mining disease-related databases for the detection of biomedical entity associations.Computational and structural biotechnology journal · 2025Article
- SciLinker: a large-scale text mining framework for mapping associations among biological entities.Frontiers in artificial intelligence · 2025Article
- Lifestyle factors in the biomedical literature: an ontology and comprehensive resources for named entity recognition.Bioinformatics (Oxford, England) · 2024Article
- HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools.Bioinformatics (Oxford, England) · 2024Article
- Improving dictionary-based named entity recognition with deep learning.Bioinformatics (Oxford, England) · 2024Article
- Integrating deep learning architectures for enhanced biomedical relation extraction: a pipeline approach.Database : the journal of biological databases and curation · 2024Article
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
- PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge.Nucleic acids research · 2024Article
- BioBBC: a multi-feature model that enhances the detection of biomedical entities.Scientific reports · 2024Article
- CoNECo: a Corpus for Named Entity recognition and normalization of protein Complexes.Bioinformatics advances · 2024Article
- Unsupervised literature mining approaches for extracting relationships pertaining to habitats and reproductive conditions of plant species.Frontiers in artificial intelligence · 2024Article
- Pancreatic cancer symptom trajectories from Danish registry data and free text in electronic health records.eLife · 2023Article
30 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The exponential growth of the biomedical literature is making the need for efficient, accurate text-mining tools increasingly clear. The identification of named biological entities in text is a central and difficult task. We have developed an efficient algorithm and implementation of a dictionary-based approach to named entity recognition, which we here use to identify names of species and other taxa in text. The tool, SPECIES, is more than an order of magnitude faster and as accurate as existing tools. The precision and recall was assessed both on an existing gold-standard corpus and on a new corpus of 800 abstracts, which were manually annotated after the development of the tool. The corpus comprises abstracts from journals selected to represent many taxonomic groups, which gives insights into which types of organism names are hard to detect and which are easy. Finally, we have tagged organism names in the entire Medline database and developed a web resource, ORGANISMS, that makes the results accessible to the broad community of biologists. The SPECIES software is open source and can be downloaded from http://species.jensenlab.org along with dictionary files and the manually annotated gold-standard corpus. The ORGANISMS web resource can be found at http://organisms.jensenlab.org.
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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 OpenQuestion graph.