Machine learning with naturally labeled data for identifying abbreviation definitions

Yeganova, Lana; Comeau, Donald C.; Wilbur, W. John

doi:10.1186/1471-2105-12-s3-s6

Cited by 10 publications

(8 citation statements)

References 11 publications

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“…Nonetheless, further research has shown that higher performances can be reached by applying machine-learning solutions either for the acronyms alone (BioADI, up to 90%) or the pairs composed of the abbreviation and its long-form (up to 91% for Ab3P; 91.4% from Yeganova et. al), which was not relevant for our rather limited experiments [46]-[48].…”

Section: Methodsmentioning

confidence: 89%

Evaluation and Cross-Comparison of Lexical Entities of Biological Interest (LexEBI)

et al. 2013

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MotivationBiomedical entities, their identifiers and names, are essential in the representation of biomedical facts and knowledge. In the same way, the complete set of biomedical and chemical terms, i.e. the biomedical “term space” (the “Lexeome”), forms a key resource to achieve the full integration of the scientific literature with biomedical data resources: any identified named entity can immediately be normalized to the correct database entry. This goal does not only require that we are aware of all existing terms, but would also profit from knowing all their senses and their semantic interpretation (ambiguities, nestedness).ResultThis study compiles a resource for lexical terms of biomedical interest in a standard format (called “LexEBI”), determines the overall number of terms, their reuse in different resources and the nestedness of terms. LexEBI comprises references for protein and gene entries and their term variants and chemical entities amongst other terms. In addition, disease terms have been identified from Medline and PubmedCentral and added to LexEBI. Our analysis demonstrates that the baseforms of terms from the different semantic types show only little polysemous use. Nonetheless, the term variants of protein and gene names (PGNs) frequently contain species mentions, which should have been avoided according to protein annotation guidelines. Furthermore, the protein and gene entities as well as the chemical entities, both do comprise enzymes leading to hierarchical polysemy, and a large portion of PGNs make reference to a chemical entity. Altogether, according to our analysis based on the Medline distribution, 401,869 unique PGNs in the documents contain a reference to 25,022 chemical entities, 3,125 disease terms or 1,576 species mentions.ConclusionLexEBI delivers the complete biomedical and chemical Lexeome in a standardized representation (http://www.ebi.ac.uk/Rebholz-srv/LexEBI/). The resource provides the disease terms as open source content, and fully interlinks terms across resources.

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Section: Methodsmentioning

confidence: 89%

Evaluation and Cross-Comparison of Lexical Entities of Biological Interest (LexEBI)

et al. 2013

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“…These rules were developed using an approximate precision measure and are adapted to the length of the abbreviation. NatLAb was developed using machine learning on a naturally labeled training set using potential definitions and random analogs ( 11 ).…”

Section: Discussionmentioning

confidence: 99%

BioC interoperability track overview

et al. 2014

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BioC is a new simple XML format for sharing biomedical text and annotations and libraries to read and write that format. This promotes the development of interoperable tools for natural language processing (NLP) of biomedical text. The interoperability track at the BioCreative IV workshop featured contributions using or highlighting the BioC format. These contributions included additional implementations of BioC, many new corpora in the format, biomedical NLP tools consuming and producing the format and online services using the format. The ease of use, broad support and rapidly growing number of tools demonstrate the need for and value of the BioC format.Database URL: http://bioc.sourceforge.net/

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“…The updated collection class object can be saved in XML format, ready to be accessed by another BioC application. The Perl BioC module has been successfully used by the NatLAb abbreviation system ( 6 ) to make it BioC compatible ( 7 ).…”

Section: Swig: a Bioc C++ Wrapper For Perl And Pythonmentioning

confidence: 99%

BioC implementations in Go, Perl, Python and Ruby

et al. 2014

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As part of a communitywide effort for evaluating text mining and information extraction systems applied to the biomedical domain, BioC is focused on the goal of interoperability, currently a major barrier to wide-scale adoption of text mining tools. BioC is a simple XML format, specified by DTD, for exchanging data for biomedical natural language processing. With initial implementations in C++ and Java, BioC provides libraries of code for reading and writing BioC text documents and annotations. We extend BioC to Perl, Python, Go and Ruby. We used SWIG to extend the C++ implementation for Perl and one Python implementation. A second Python implementation and the Ruby implementation use native data structures and libraries. BioC is also implemented in the Google language Go. BioC modules are functional in all of these languages, which can facilitate text mining tasks. BioC implementations are freely available through the BioC site: http://bioc.sourceforge.net.Database URL: http://bioc.sourceforge.net/

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Machine learning with naturally labeled data for identifying abbreviation definitions

Cited by 10 publications

References 11 publications

Evaluation and Cross-Comparison of Lexical Entities of Biological Interest (LexEBI)

Evaluation and Cross-Comparison of Lexical Entities of Biological Interest (LexEBI)

BioC interoperability track overview

BioC implementations in Go, Perl, Python and Ruby

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