Abstract
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/.
Highlights
The 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
Tagging of the document sets took approximately 5 hours for MEDLINE, 2.5 hours for PubMed Central (PMC) OA abstracts and 4 hours for PMC OA, utilizing four Intel Xeon 3 GHz CPU cores and 4 GB memory. (We note that the main factor influencing processing time is the Java XML document parsing rather than the actual species name tagging.) These species tagging experiments far exceed the scale of any previous report [7,10,14,23,25,36,37,41], and represent one of the first applications of text mining to the entire PMC OA corpus
Over 30 million species tags for over 57,000 different species were detected in MEDLINE, and over 4 million species tags for nearly 19,000 species in PMC OA
Summary
The 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. The amount of biomedical literature available to researchers is growing exponentially, with over 18 million article entries available in MEDLINE [1] and over a million full-text articles freely available in PubMed Central (PMC) [2]. A wide variety of biomedical text-mining tasks are currently being pursued (reviewed in [3,4]), such as entity recognition (e.g. finding mentions of genes, proteins, diseases) and extraction of molecular relationships (e.g. protein-protein interactions). Many of these systems are constructed in a modular fashion and rely on the results of other text-mining applications. Likewise, improved methods for identifying species names can assist pipelines that integrate biological data using species names as identifiers [11,12]
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