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From chen...@apache.org
Subject svn commit: r1360958 - /incubator/ctakes/site/trunk/content/ctakes/index.mdtext
Date Thu, 12 Jul 2012 21:27:43 GMT
Author: chenpei
Date: Thu Jul 12 21:27:43 2012
New Revision: 1360958

URL: http://svn.apache.org/viewvc?rev=1360958&view=rev
Log:
initial content for the Apache cTAKES home page.

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

Modified: incubator/ctakes/site/trunk/content/ctakes/index.mdtext
URL: http://svn.apache.org/viewvc/incubator/ctakes/site/trunk/content/ctakes/index.mdtext?rev=1360958&r1=1360957&r2=1360958&view=diff
==============================================================================
--- incubator/ctakes/site/trunk/content/ctakes/index.mdtext (original)
+++ incubator/ctakes/site/trunk/content/ctakes/index.mdtext Thu Jul 12 21:27:43 2012
@@ -1,4 +1,4 @@
-Title:     Home Page
+Title:     Apache cTAKES
 Notice:    Licensed to the Apache Software Foundation (ASF) under one
            or more contributor license agreements.  See the NOTICE file
            distributed with this work for additional information
@@ -18,7 +18,22 @@ Notice:    Licensed to the Apache Softwa
 
 # Welcome
 
-Welcome to the Apache CMS.  Please see the following resources for further help:
+Welcome to the Apache cTAKES.
+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.
 
- - <http://www.apache.org/dev/cmsref.html>
- - <http://wiki.apache.org/general/ApacheCms2010>
+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.
+
+These components include:
+
+  - Sentence boundary detector
+  - Rule-based tokenizer to separate punctuations from words
+  - Normalizer
+  - Context dependent tokenizer
+  - Part-of-speech tagger
+  - Phrasal chunker
+  - Dictionary lookup annotator
+  - Context annotator
+  - Negation detector
+  - Dependency parser
+  - Module for the identification of patient smoking status
+  - Drug mention annotator



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