LINNAEUS: A species name identification system for biomedical literature

By Martin Gerner, Goran Nenadic, Casey M. Bergman. BMC Bioinformatics, 2010

392 citations recorded by the source corpus.

BackgroundThe task of recognizing and identifying species names in biomedical literature has recently been regarded as critical for a number of applications in text and data mining, including gene name recognition, species-specific document retrieval, and semantic enrichment of biomedical articles.ResultsIn this paper we describe an open-source species name recognition and normalization software system, LINNAEUS, and evaluate its performance relative to several automatically generated biomedical corpora, as well as a novel corpus of full-text documents manually annotated for species mentions. LINNAEUS uses a dictionary-based approach (implemented as an efficient deterministic finite-state automaton) to identify species names and a set of heuristics to resolve ambiguous mentions. When compared against our manually annotated corpus, LINNAEUS performs with 94% recall and 97% precision at the mention level, and 98% recall and 90% precision at the document level. Our system successfully solves the problem of disambiguating uncertain species mentions, with 97% of all mentions in PubMed Central full-text documents resolved to unambiguous NCBI taxonomy identifiers.ConclusionsLINNAEUS is an open source, stand-alone software system capable of recognizing and normalizing species name mentions with speed and accuracy, and can therefore be integrated into a range of bioinformatics and text-mining applications. The software and manually annotated corpus can be downloaded freely at http://linnaeus.sourceforge.net/.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e1039346942874c59d89028bb934b72524827b8c

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Citations in the corpus

The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text (2013, background): “with synonyms, a stop-word list to filter out matches to common English words, and post-processing rules to disambiguate homonyms [6].”
The CHEMDNER corpus of chemicals and drugs and its annotation principles (2015, background): “Text mining and information extraction techniques are showing promising results in the biomedical domain: A range of applications have been implemented [2] to detect bio-entities [3,4] and their relations (e.”
miRBase: from microRNA sequences to function (2018, background): “Recognizing gene names, or even species names, in full-text articles is surprisingly challenging (28–30).”
PubTator central: automated concept annotation for biomedical full text articles (2019, methodology): “The taggers for two concept types were evaluated on full text corpora (genetic variants (24) and species (43)), while the tagger for cell lines was evaluated on a corpus derived from figure captions (44).”
ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing (2019, methodology): “In particular, we retrain the spaCy NER model on each of the four datasets mentioned earlier (BC5CDR, CRAFT, JNLPBA, BioNLP13CG) as well as ﬁve more datasets in Crichton et al. (2017): AnatEM, BC2GM, BC4CHEMD, Linnaeus, NCBI-Disease.”
BioBERT: a pre-trained biomedical language representation model for biomedical text mining (2019, background): “, 2018) for a fair evaluation; however, the splits of LINAAEUS and Species-800 could not be found from Giorgi and Bader (2018) and may be different.”
The taxonomic name resolution service: an online tool for automated standardization of plant names (2013, background): “Recent applications for the automated recognition of taxon names [34] have accelerated the digitization of biodiversity literature [35].”
CollaboNet: collaboration of deep neural networks for biomedical named entity recognition (2018, methodology): “For example, CRAFT [42], LINNAEUS [43] and Variome [44] are manually annotated datasets and are valuable resources that can be used for expanding our model.”