Linked Life Data

17050571

Source:http://linkedlifedata.com/resource/pubmed/id/17050571

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
24
pubmed:dateCreated
2006-12-4
pubmed:abstractText
MOTIVATION: Acronyms result from a highly productive type of term variation and trigger the need for an acronym dictionary to establish associations between acronyms and their expanded forms. RESULTS: We propose a novel method for recognizing acronym definitions in a text collection. Assuming a word sequence co-occurring frequently with a parenthetical expression to be a potential expanded form, our method identifies acronym definitions in a similar manner to the statistical term recognition task. Applied to the whole MEDLINE (7 811 582 abstracts), the implemented system extracted 886 755 acronym candidates and recognized 300 954 expanded forms in reasonable time. Our method outperformed base-line systems, achieving 99% precision and 82-95% recall on our evaluation corpus that roughly emulates the whole MEDLINE. AVAILABILITY AND SUPPLEMENTARY INFORMATION: The implementations and supplementary information are available at our web site: http://www.chokkan.org/research/acromine/
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
1367-4811
pubmed:author
pubmed:issnType
Electronic
pubmed:day
15
pubmed:volume
22
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
3089-95
pubmed:dateRevised
2009-11-4
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Building an abbreviation dictionary using a term recognition approach.
pubmed:affiliation
Graduate School of Information Science and Technology, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8651, Japan. okazaki@mi.ci.i.u-tokyo.ac.jp
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't