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Items: 1 to 20 of 117

1.

A study of abbreviations in MEDLINE abstracts.

Liu H, Aronson AR, Friedman C.

Proc AMIA Symp. 2002:464-8.

2.

Automatic resolution of ambiguous terms based on machine learning and conceptual relations in the UMLS.

Liu H, Johnson SB, Friedman C.

J Am Med Inform Assoc. 2002 Nov-Dec;9(6):621-36.

3.

Resolving abbreviations to their senses in Medline.

Gaudan S, Kirsch H, Rebholz-Schuhmann D.

Bioinformatics. 2005 Sep 15;21(18):3658-64. Epub 2005 Jul 21.

PMID:
16037121
4.

A study of abbreviations in the UMLS.

Liu H, Lussier YA, Friedman C.

Proc AMIA Symp. 2001:393-7.

5.

A study of abbreviations in clinical notes.

Xu H, Stetson PD, Friedman C.

AMIA Annu Symp Proc. 2007 Oct 11:821-5.

6.

Exploiting MeSH indexing in MEDLINE to generate a data set for word sense disambiguation.

Jimeno-Yepes AJ, McInnes BT, Aronson AR.

BMC Bioinformatics. 2011 Jun 2;12:223. doi: 10.1186/1471-2105-12-223.

7.

Mining terminological knowledge in large biomedical corpora.

Liu H, Friedman C.

Pac Symp Biocomput. 2003:415-26.

8.

A sense inventory for clinical abbreviations and acronyms created using clinical notes and medical dictionary resources.

Moon S, Pakhomov S, Liu N, Ryan JO, Melton GB.

J Am Med Inform Assoc. 2014 Mar-Apr;21(2):299-307. doi: 10.1136/amiajnl-2012-001506. Epub 2013 Jun 27.

9.

A new clustering method for detecting rare senses of abbreviations in clinical notes.

Xu H, Wu Y, Elhadad N, Stetson PD, Friedman C.

J Biomed Inform. 2012 Dec;45(6):1075-83. doi: 10.1016/j.jbi.2012.06.003. Epub 2012 Jun 25.

10.

Disambiguation in the biomedical domain: the role of ambiguity type.

Stevenson M, Guo Y.

J Biomed Inform. 2010 Dec;43(6):972-81. doi: 10.1016/j.jbi.2010.08.009. Epub 2010 Sep 9.

11.

Creating an online dictionary of abbreviations from MEDLINE.

Chang JT, Sch├╝tze H, Altman RB.

J Am Med Inform Assoc. 2002 Nov-Dec;9(6):612-20.

12.

Using UMLS lexical resources to disambiguate abbreviations in clinical text.

Kim Y, Hurdle J, Meystre SM.

AMIA Annu Symp Proc. 2011;2011:715-22. Epub 2011 Oct 22.

13.

ADAM: another database of abbreviations in MEDLINE.

Zhou W, Torvik VI, Smalheiser NR.

Bioinformatics. 2006 Nov 15;22(22):2813-8. Epub 2006 Sep 18.

PMID:
16982707
14.

Building an abbreviation dictionary using a term recognition approach.

Okazaki N, Ananiadou S.

Bioinformatics. 2006 Dec 15;22(24):3089-95. Epub 2006 Oct 18.

PMID:
17050571
15.

Link-topic model for biomedical abbreviation disambiguation.

Kim S, Yoon J.

J Biomed Inform. 2015 Feb;53:367-80. doi: 10.1016/j.jbi.2014.12.013. Epub 2014 Dec 30.

16.

Developing a test collection for biomedical word sense disambiguation.

Weeber M, Mork JG, Aronson AR.

Proc AMIA Symp. 2001:746-50.

17.

MEDTAG: tag-like semantics for medical document indexing.

Ruch P, Wagner J, Bouillon P, Baud RH, Rassinoux AM, Scherrer JR.

Proc AMIA Symp. 1999:137-41.

18.

Ambiguity resolution while mapping free text to the UMLS Metathesaurus.

Rindflesch TC, Aronson AR.

Proc Annu Symp Comput Appl Med Care. 1994:240-4.

19.

Using MEDLINE as a knowledge source for disambiguating abbreviations and acronyms in full-text biomedical journal articles.

Yu H, Kim W, Hatzivassiloglou V, Wilbur WJ.

J Biomed Inform. 2007 Apr;40(2):150-9. Epub 2006 Jun 7.

20.

Determining the difficulty of Word Sense Disambiguation.

McInnes BT, Stevenson M.

J Biomed Inform. 2014 Feb;47:83-90. doi: 10.1016/j.jbi.2013.09.009. Epub 2013 Sep 26.

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