Literature DB >> 14728170

Integrating a hypernymic proposition interpreter into a semantic processor for biomedical texts.

Marcelo Fiszman1, Thomas C Rindflesch, Halil Kilicoglu.   

Abstract

Semantic processing provides the potential for producing high quality results in natural language processing (NLP) applications in the biomedical domain. In this paper, we address a specific semantic phenomenon, the hypernymic proposition, and concentrate on integrating the interpretation of such predications into a more general semantic processor in order to improve overall accuracy. A preliminary evaluation assesses the contribution of hypernymic propositions in providing more specific semantic predications and thus improving effectiveness in retrieving treatment propositions in MEDLINE abstracts. Finally, we discuss the generalization of this methodology to additional semantic propositions as well as other types of biomedical texts.

Mesh:

Year:  2003        PMID: 14728170      PMCID: PMC1479962     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN: 1559-4076


  14 in total

1.  A reliability study for evaluating information extraction from radiology reports.

Authors:  G Hripcsak; G J Kuperman; C Friedman; D F Heitjan
Journal:  J Am Med Inform Assoc       Date:  1999 Mar-Apr       Impact factor: 4.497

2.  Argument identification for arterial branching predications asserted in cardiac catheterization reports.

Authors:  T C Rindflesch; C A Bean; C A Sneiderman
Journal:  Proc AMIA Symp       Date:  2000

3.  MedSynDiKATe--design considerations for an ontology-based medical text understanding system.

Authors:  U Hahn; M Romacker; S Schulz
Journal:  Proc AMIA Symp       Date:  2000

4.  A broad-coverage natural language processing system.

Authors:  C Friedman
Journal:  Proc AMIA Symp       Date:  2000

5.  Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program.

Authors:  A R Aronson
Journal:  Proc AMIA Symp       Date:  2001

6.  Aggregating UMLS semantic types for reducing conceptual complexity.

Authors:  A T McCray; A Burgun; O Bodenreider
Journal:  Stud Health Technol Inform       Date:  2001

7.  Exploring text mining from MEDLINE.

Authors:  Padmini Srinivasan; Thomas Rindflesch
Journal:  Proc AMIA Symp       Date:  2002

8.  The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text.

Authors:  Thomas C Rindflesch; Marcelo Fiszman
Journal:  J Biomed Inform       Date:  2003-12       Impact factor: 6.317

9.  Semantic relations asserting the etiology of genetic diseases.

Authors:  Thomas C Rindflesch; Bisharah Libbus; Dimitar Hristovski; Alan R Aronson; Halil Kilicoglu
Journal:  AMIA Annu Symp Proc       Date:  2003

10.  Interpreting natural language queries using the UMLS.

Authors:  S B Johnson; A Aguirre; P Peng; J Cimino
Journal:  Proc Annu Symp Comput Appl Med Care       Date:  1993
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  6 in total

Review 1.  Natural Language Processing methods and systems for biomedical ontology learning.

Authors:  Kaihong Liu; William R Hogan; Rebecca S Crowley
Journal:  J Biomed Inform       Date:  2010-07-18       Impact factor: 6.317

2.  Effectiveness of lexico-syntactic pattern matching for ontology enrichment with clinical documents.

Authors:  K Liu; W W Chapman; G Savova; C G Chute; N Sioutos; R S Crowley
Journal:  Methods Inf Med       Date:  2010-11-08       Impact factor: 2.176

3.  MachineProse: an ontological framework for scientific assertions.

Authors:  Deendayal Dinakarpandian; Yugyung Lee; Kartik Vishwanath; Rohini Lingambhotla
Journal:  J Am Med Inform Assoc       Date:  2005-12-15       Impact factor: 4.497

4.  Knowledge-based methods to help clinicians find answers in MEDLINE.

Authors:  Charles A Sneiderman; Dina Demner-Fushman; Marcelo Fiszman; Nicholas C Ide; Thomas C Rindflesch
Journal:  J Am Med Inform Assoc       Date:  2007-08-21       Impact factor: 4.497

5.  Assessing the role of a medication-indication resource in the treatment relation extraction from clinical text.

Authors:  Cosmin Adrian Bejan; Wei-Qi Wei; Joshua C Denny
Journal:  J Am Med Inform Assoc       Date:  2014-10-21       Impact factor: 4.497

6.  MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge.

Authors:  Ali Z Ijaz; Min Song; Doheon Lee
Journal:  BMC Bioinformatics       Date:  2010-04-16       Impact factor: 3.169

  6 in total

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