Return-Path: X-Original-To: apmail-incubator-ctakes-commits-archive@minotaur.apache.org Delivered-To: apmail-incubator-ctakes-commits-archive@minotaur.apache.org Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by minotaur.apache.org (Postfix) with SMTP id 2487AEF5A for ; Tue, 4 Dec 2012 22:12:13 +0000 (UTC) Received: (qmail 17372 invoked by uid 500); 4 Dec 2012 22:12:13 -0000 Delivered-To: apmail-incubator-ctakes-commits-archive@incubator.apache.org Received: (qmail 17345 invoked by uid 500); 4 Dec 2012 22:12:13 -0000 Mailing-List: contact ctakes-commits-help@incubator.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Reply-To: ctakes-dev@incubator.apache.org Delivered-To: mailing list ctakes-commits@incubator.apache.org Received: (qmail 17337 invoked by uid 99); 4 Dec 2012 22:12:13 -0000 Received: from nike.apache.org (HELO nike.apache.org) (192.87.106.230) by apache.org (qpsmtpd/0.29) with ESMTP; Tue, 04 Dec 2012 22:12:13 +0000 X-ASF-Spam-Status: No, hits=-2000.0 required=5.0 tests=ALL_TRUSTED X-Spam-Check-By: apache.org Received: from [140.211.11.4] (HELO eris.apache.org) (140.211.11.4) by apache.org (qpsmtpd/0.29) with ESMTP; Tue, 04 Dec 2012 22:12:05 +0000 Received: from eris.apache.org (localhost [127.0.0.1]) by eris.apache.org (Postfix) with ESMTP id 51935238897F for ; Tue, 4 Dec 2012 22:11:44 +0000 (UTC) Content-Type: text/plain; charset="utf-8" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit Subject: svn commit: r840768 - in /websites/staging/ctakes/trunk/content: ./ ctakes/components.html Date: Tue, 04 Dec 2012 22:11:44 -0000 To: ctakes-commits@incubator.apache.org From: buildbot@apache.org X-Mailer: svnmailer-1.0.8-patched Message-Id: <20121204221144.51935238897F@eris.apache.org> X-Virus-Checked: Checked by ClamAV on apache.org Author: buildbot Date: Tue Dec 4 22:11:43 2012 New Revision: 840768 Log: Staging update by buildbot for ctakes Modified: websites/staging/ctakes/trunk/content/ (props changed) websites/staging/ctakes/trunk/content/ctakes/components.html Propchange: websites/staging/ctakes/trunk/content/ ------------------------------------------------------------------------------ --- cms:source-revision (original) +++ cms:source-revision Tue Dec 4 22:11:43 2012 @@ -1 +1 @@ -1417005 +1417219 Modified: websites/staging/ctakes/trunk/content/ctakes/components.html ============================================================================== --- websites/staging/ctakes/trunk/content/ctakes/components.html (original) +++ websites/staging/ctakes/trunk/content/ctakes/components.html Tue Dec 4 22:11:43 2012 @@ -89,13 +89,15 @@

Apache OpenNLP technology with a model trained on manually annotated clinical data (see Savova et al, 2010)

Tokenization

(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

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

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)

Co-reference resolver

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.

Choi J, Palmer M. Transition-based Semantic Role Labeling Using Predicate Argument Clustering. RELMS 2011: Relational Models of Semantics, held in conjunction with ACL-HLT 2011. Portland, OR, 2011b.