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Guy Divita Qing Zeng- Trei tler Salt Lake City VA, University of Utah School of Medicine

Pragmatic Interoperability. Guy Divita Qing Zeng- Trei tler Salt Lake City VA, University of Utah School of Medicine. Toy Story. We all agree one-off-solution is not good Many approaches to Interoperability. Internal Interoperabil ity. Framework Supporting Interoperability.

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Guy Divita Qing Zeng- Trei tler Salt Lake City VA, University of Utah School of Medicine

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  1. Pragmatic Interoperability Guy Divita Qing Zeng-Treitler Salt Lake City VA, University of Utah School of Medicine

  2. Toy Story • We all agree one-off-solution is not good • Many approaches to Interoperability

  3. Internal Interoperability

  4. Framework Supporting Interoperability

  5. Within System Issues Many Master Builders who do not share a plan! And plan changes…

  6. Within System Issues Sometimes integration is more work than building from scrtach

  7. Pragmatic Interoperability

  8. Overview

  9. Terminology

  10. Messaging Protocol

  11. Ontological Alignment

  12. Pragmatics

  13. Pragmatics (2)

  14. Feature Alignment Semantics

  15. Feature Alignment Semantics (2)

  16. Expressibility

  17. Expressibility (2)

  18. Expressibility(3)

  19. Pragmatics (again)

  20. Interoperability Efforts within v3NLP

  21. Common Set of Labels • From survey of existing NLP systems • Common ontological referent points • Combines CDA, Penn Treebank labels • New labels where needed • Document structure (table, figure, snippet, slot value … • Rendered into • UIMA Type Descriptors • Protégée Ontology

  22. Interoperability Efforts within v3NLP

  23. Special Considerations

  24. Modeling Negation

  25. Asymmetric Interoperability

  26. Barriers to Interoperability

  27. Solution Some planning Simple protocol + Simple semantics Limit the number of moving parts

  28. Pragmatic Interoperability Principles

  29. NLP Ecosystem Elements • Programs, projects, ideas, utilities, services and shared resources • workflow aids, annotation editors, evaluation tools, lexicon generation aids, ontology development tools, dashboards • Repo’s, wiki’s, website(s) • Evolving standards, guidelines, best practices

  30. NLP Ecosystem Elements (continued) • Big goals, grand challenges, many little dance steps • Engendering developer and user communities • Engendering collaborative environments • Governing body to influence direction • ……

  31. Clinical NLP Development Trend

  32. … Toward a NLP Ecosystem Voogo cTAKES WEKA MetaMap Mallot RapTAT U Compare Sublanguage Model UIMA ARC Common Labels UIMA-GATE converter UFIT Dave EMMA Sophia

  33. An Ecosystem that includes POS Tagger Corpus Annotation Analysis Text-To-Structured Data Annotation Tools Text Normalization (stemming) GATE Concordance Template Detection Negation Shallow NP Parser Relationship Identification UIMA Full Parser Temporal Relations Section Tokenizer Knowtator Text Simplification Document / Section / Sentence/ Phrase Information Retrieval indexes Local Terminology Development Tool NLP Standards Document Classifier Spelling Suggestion Concept: value Identification De-Identification Co-reference Resolution Term Identification Table Tokenization Word Sense Disambiguation Concept Identification Figure/Caption Identification Anaphora Resolution Semantic Classification Theme Detection Multi-Document Summarization – Novelty Detection Document Summarization

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