svn commit: r829220 - in /websites/staging/ctakes/trunk/content: ./ ctakes/index.html

[bu...@apache.org]

Author: buildbot
Date: Fri Aug 17 15:09:44 2012
New Revision: 829220

Log:
Staging update by buildbot for ctakes

Modified:
    websites/staging/ctakes/trunk/content/   (props changed)
    websites/staging/ctakes/trunk/content/ctakes/index.html

Propchange: websites/staging/ctakes/trunk/content/
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--- cms:source-revision (original)
+++ cms:source-revision Fri Aug 17 15:09:44 2012
@@ -1 +1 @@
-1374197
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Modified: websites/staging/ctakes/trunk/content/ctakes/index.html
==============================================================================
--- websites/staging/ctakes/trunk/content/ctakes/index.html (original)
+++ websites/staging/ctakes/trunk/content/ctakes/index.html Fri Aug 17 15:09:44 2012
@@ -76,13 +76,13 @@
   </div>
   <div id="contenta">
     <h1 id="welcome-to-apache-ctakes">Welcome to Apache cTAKES.</h1>
-<p>Apache cTAKES: clinical Text Analysis and Knowledge Extraction System is an open-source natural language processing system for information extraction from electronic medical record clinical free-text. It processes clinical notes, identifying types of clinical named entities — drugs, diseases/disorders, signs/symptoms, anatomical sites and procedures. Each named entity has attributes for the text span, the ontology mapping code, context (family history of, current, unrelated to patient), and negated/not negated.</p>
+<p>Apache cTAKES: clinical Text Analysis and Knowledge Extraction System is an open-source natural language processing system for information extraction from electronic medical record clinical free-text. It processes clinical notes, identifying types of clinical named entities - medications, diseases/disorders, signs/symptoms, anatomical sites and procedures. Each named entity has attributes for the text span, the ontology mapping code, subject (patient, family member, etc.) and context (negated/not negated, conditional, generic).</p>
 <p>Apache cTAKES was built using the UIMA Unstructured Information Management Architecture framework and OpenNLP natural language processing toolkit. Its components are specifically trained for the clinical domain, and create rich linguistic and semantic annotations that can be utilized by clinical decision support systems and clinical research.</p>
 <p>These components include:</p>
 <ul>
 <li>Sentence boundary detection (OpenNLP technology)</li>
 <li>Tokenization (rule-based)</li>
-<li>Morphologic normalization (NLM’s LVG)</li>
+<li>Morphologic normalization (NLM's LVG)</li>
 <li>POS tagging (OpenNLP technology)</li>
 <li>Shallow parsing (OpenNLP technology)</li>
 <li>Named Entity Recognition<ol>