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A Distributed Framework for
Computation on the Results of
      Large Scale NLP
 Christophe Roeder, William A. Baumgartner Jr., Kevin Livingston,
                         Lawrence E. Hunter
         (University of Colorado Anschutz Medial Campus)




                                    Chris.Roeder@ucdenver.edu
                                    http://compbio.ucdenver.edu
Motivation
• A vast amount of information is available
    in journal articles
•   Journal articles are unstructured text
•   Many applications require structured
    knowledge
    – Curated ontologies (Gene Ontology)
    – Databases (UniProt, EntrezGene)
• Challenge: extract structured knowledge
    from unstructured text and integrate with
    existing knowledge…at massive scale
Architecture
Journal                                                  RDF
                      Scaled NLP Pipeline
Articles(u                                            Document
nstructured)                                           s(structured)
                                            Queries      Sesam
                       Knowledge                         e/Hado
                      Base(Ontologi                        op
                           es,
                       Databases)

                                 Knowledg
 Applications
   Applications
(Visualization
                                 e                     Distilled
      Applications
  (Visualization
   , (Visualization
      NLP,…)
      , NLP,…)
                                                       Output
        , NLP,…)                                       (structured)
                          Structured
                          Information
Example Application
• Concept annotation
  trends over time

                                        Insuli
                                        n


                                         NOS1




        http://tinyurl.com/bio-trends
Summary
•   NLP pipelines extract structured annotations
•   Our framework provides massively parallel access
    to these structured document annotations
•   Structured representation is integrated with
    knowledge base
•   Affords parallelization when possible, and access
    to knowledge base when necessary
•   Provides integration of unstructured document text
    with structured knowledge for enabling
    applications such as:
    – Visualization (BioJigsaw, Hanalyzer,…)
    – Natural Language Understanding (OpenDMAP)
    – Leveraging text data for validation and evaluation of
      other methods
Thank You / Questions
•   http://tinyurl.com/bio-trends

•   Co-authors
    – William A. Baumgartner Jr. for data generation
    – Kevin Livingston for RDF and Clojure help
•   Grants and PIs
    – Lawrence E Hunter, UCDenver SOM
        • NIH 2R01LM009254-04, NIH 2R01LM008111-04A1,
         NIH 5R01GM083649-02
    – Karin Verspoor, UCDenver SOM
        • NIH R01 LM010120-01
    – Gully Burns, ISI
        • NSF 0849977

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Roeder rocky 2011_46

  • 1. A Distributed Framework for Computation on the Results of Large Scale NLP Christophe Roeder, William A. Baumgartner Jr., Kevin Livingston, Lawrence E. Hunter (University of Colorado Anschutz Medial Campus) Chris.Roeder@ucdenver.edu http://compbio.ucdenver.edu
  • 2. Motivation • A vast amount of information is available in journal articles • Journal articles are unstructured text • Many applications require structured knowledge – Curated ontologies (Gene Ontology) – Databases (UniProt, EntrezGene) • Challenge: extract structured knowledge from unstructured text and integrate with existing knowledge…at massive scale
  • 3. Architecture Journal RDF Scaled NLP Pipeline Articles(u Document nstructured) s(structured) Queries Sesam Knowledge e/Hado Base(Ontologi op es, Databases) Knowledg Applications Applications (Visualization e Distilled Applications (Visualization , (Visualization NLP,…) , NLP,…) Output , NLP,…) (structured) Structured Information
  • 4. Example Application • Concept annotation trends over time Insuli n NOS1 http://tinyurl.com/bio-trends
  • 5. Summary • NLP pipelines extract structured annotations • Our framework provides massively parallel access to these structured document annotations • Structured representation is integrated with knowledge base • Affords parallelization when possible, and access to knowledge base when necessary • Provides integration of unstructured document text with structured knowledge for enabling applications such as: – Visualization (BioJigsaw, Hanalyzer,…) – Natural Language Understanding (OpenDMAP) – Leveraging text data for validation and evaluation of other methods
  • 6. Thank You / Questions • http://tinyurl.com/bio-trends • Co-authors – William A. Baumgartner Jr. for data generation – Kevin Livingston for RDF and Clojure help • Grants and PIs – Lawrence E Hunter, UCDenver SOM • NIH 2R01LM009254-04, NIH 2R01LM008111-04A1, NIH 5R01GM083649-02 – Karin Verspoor, UCDenver SOM • NIH R01 LM010120-01 – Gully Burns, ISI • NSF 0849977

Notas del editor

  1. Plug KabobPlug Open Access, Mention Elsevier collections, size
  2. Mention UIMA Distringuish NER from normalization, and how that ID ties it into the KBPutting High Precision Enttiytrecog to work at large scaleInduction, abductionGet around noise issues by using a LOT of dataPrecision and recal require scaleMight learn something, if said often enoughCorrleations between proteins, coorrenceppiCoorrence with other ontology terms or other extracted terms or biological processes
  3. No excuses, don’t trivialize, but emphasize its value as a demoBuilt in about a week, computation over PMC OA in 2 hours on a very modest cluster (40 cores)(inefficiencies exist as well) lot of data, runs qucilyDemonstrates that the framework can be used quickly and worksSame technology can be used
  4. On that last point, think of coorelatoins and stuff.** who knows what we’ll think of with the possibilities this opens up