Linked Life Data

11977807

Source:http://linkedlifedata.com/resource/pubmed/id/11977807

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2002-4-29
pubmed:abstractText
With the growing use of Natural Language Processing (NLP) techniques for information extraction and concept indexing in the biomedical domain, a method that quickly and efficiently assigns the correct sense of an ambiguous biomedical term in a given context is needed concurrently. The current status of word sense disambiguation (WSD) in the biomedical domain is that handcrafted rules are used based on contextual material. The disadvantages of this approach are (i) generating WSD rules manually is a time-consuming and tedious task, (ii) maintenance of rule sets becomes increasingly difficult over time, and (iii) handcrafted rules are often incomplete and perform poorly in new domains comprised of specialized vocabularies and different genres of text. This paper presents a two-phase unsupervised method to build a WSD classifier for an ambiguous biomedical term W. The first phase automatically creates a sense-tagged corpus for W, and the second phase derives a classifier for W using the derived sense-tagged corpus as a training set. A formative experiment was performed, which demonstrated that classifiers trained on the derived sense-tagged corpora achieved an overall accuracy of about 97%, with greater than 90% accuracy for each individual ambiguous term.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1532-0464
pubmed:author
pubmed:issnType
Print
pubmed:volume
34
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
249-61
pubmed:dateRevised
2010-6-11
pubmed:meshHeading
pubmed:year
2001
pubmed:articleTitle
Disambiguating ambiguous biomedical terms in biomedical narrative text: an unsupervised method.
pubmed:affiliation
Computer Science Division, Graduate School and University Center, City University of New York, New York, New York 10016, USA. hol7001@dmi.columbia.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, U.S. Gov't, Non-P.H.S.