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From chen...@apache.org
Subject svn commit: r1417219 - /incubator/ctakes/site/trunk/content/ctakes/components.mdtext
Date Tue, 04 Dec 2012 22:11:37 GMT
Author: chenpei
Date: Tue Dec  4 22:11:36 2012
New Revision: 1417219

URL: http://svn.apache.org/viewvc?rev=1417219&view=rev
Log:
CMS commit to ctakes by chenpei

Modified:
    incubator/ctakes/site/trunk/content/ctakes/components.mdtext

Modified: incubator/ctakes/site/trunk/content/ctakes/components.mdtext
URL: http://svn.apache.org/viewvc/incubator/ctakes/site/trunk/content/ctakes/components.mdtext?rev=1417219&r1=1417218&r2=1417219&view=diff
==============================================================================
--- incubator/ctakes/site/trunk/content/ctakes/components.mdtext (original)
+++ incubator/ctakes/site/trunk/content/ctakes/components.mdtext Tue Dec  4 22:11:36 2012
@@ -23,16 +23,18 @@ Apache OpenNLP technology with a model t
 (see Savova et al, 2010)
 
 ### Tokenization
-(rule-based) (see Savova et al, 2010)
+Rule-based (see Savova et al, 2010)
 
-### Morphologic normalization (National Library of Medicine's Lexical Variant Generation
tool
-http://www.nlm.nih.gov/research/umls/new_users/online_learning/LEX_004.htm
+### Morphologic normalization (National Library of Medicine's Lexical Variant Generation
tool)
+[http://www.nlm.nih.gov/research/umls/new_users/online_learning/LEX_004.htm](http://www.nlm.nih.gov/research/umls/new_users/online_learning/LEX_004.htm)
 
 ### POS tagging 
-Apache OpenNLP technology with a model trained on manually annotated clinical data) (see
Savova et al, 2010; upcoming 2013 publication
+Apache OpenNLP technology with a model trained on manually annotated clinical data
+(see Savova et al, 2010; upcoming 2013 publication)
 
 ### Shallow parsing 
-(Apache OpenNLP technology with a model trained on manually annotated clinical data) (see
Savova et al, 2010)
+Apache OpenNLP technology with a model trained on manually annotated clinical data
+(see Savova et al, 2010)
 
 ### Named Entity Recognition (see Savova et al, 2010)
    - Dictionary mapping (lookup algorithm)
@@ -54,13 +56,13 @@ Assigns the predicate-argument structure
 Resolves co-referring entities. (machine learning with a model trained on manually annotated
clinical data) (see Zheng et al, 2011)
 
 ### Relation extractor
-discovers such attributes as the location and the severity of a clinical condition (machine
learning with a model trained on manually annotated clinical data) (upcoming 2013 publication)
+Discovers attributes such as the location and the severity of a clinical condition (machine
learning with a model trained on manually annotated clinical data) (upcoming 2013 publication)
 
 ### Drug Profile module
-discovers drug-specific attributes such as dosage, duration, form, frequency, route, strength
(see Sohn et al, 2010; Savova et al, 2011)
+Discovers drug-specific attributes such as dosage, duration, form, frequency, route, strength
(see Sohn et al, 2010; Savova et al, 2011)
 
 ### Smoking status classifier
-past smoker, current smoker, non-smoker, smoker, unknown (see Savova et al, 2008)
+Classifies document/patient as past smoker, current smoker, non-smoker, smoker, unknown (see
Savova et al, 2008)
 
 # Select Publications:
 Choi J, Palmer M. Getting the most out of Transition-based Dependency Parsing. 46th Annual
Meeting of the Association for Computational Linguistics: Human Language Technologies,. Portland,
OR.: ACL-HLT 2011a , 2011.
@@ -75,4 +77,4 @@ Savova G, Ogren P, Duffy P, Buntrock J a
 
 Sohn S, Murphy SP, Masanz JJ, Kocher JP, Savova GK. Classification of medication status change
in clinical narratives. AMIA Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
2010;2010:762-6.
 
-Zheng J, Chapman W, Miller T, Lin C, Crowley R and Savova G. 2012. A system for coreference
resolution for the clinical narrative. Journal of the American Medical Informatics Association.
doi:10.1136/amiajnl-2011-000599
+Zheng J, Chapman W, Miller T, Lin C, Crowley R and Savova G. 2012. A system for coreference
resolution for the clinical narrative. Journal of the American Medical Informatics Association.
doi:10.1136/amiajnl-2011-000599
\ No newline at end of file



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