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rdf:type |
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lifeskim:mentions |
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pubmed:dateCreated |
2008-3-4
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pubmed:abstractText |
A biomedical entity mention in articles and other free texts is often ambiguous. For example, 13% of the gene names (aliases) might refer to more than one gene. The task of Gene Symbol Disambiguation (GSD) - a special case of Word Sense Disambiguation (WSD) - is to assign a unique gene identifier for all identified gene name aliases in biology-related articles. Supervised and unsupervised machine learning WSD techniques have been applied in the biomedical field with promising results. We examine here the utilisation potential of the fact - one of the special features of biological articles - that the authors of the documents are known through graph-based semi-supervised methods for the GSD task.
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pubmed:commentsCorrections |
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pubmed:language |
eng
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pubmed:journal |
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pubmed:citationSubset |
IM
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pubmed:status |
MEDLINE
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pubmed:issn |
1471-2105
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pubmed:author |
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pubmed:issnType |
Electronic
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pubmed:volume |
9
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pubmed:owner |
NLM
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pubmed:authorsComplete |
Y
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pubmed:pagination |
69
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pubmed:dateRevised |
2009-11-18
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pubmed:meshHeading |
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pubmed:year |
2008
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pubmed:articleTitle |
The strength of co-authorship in gene name disambiguation.
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pubmed:affiliation |
Hungarian Academy of Science, Research Group on Artificial Intelligence, Aradi vertanuk tere, Szeged, Hungary. rfarkas@inf.u-szeged.hu
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pubmed:publicationType |
Journal Article
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