eagle-i Oregon Health & Science UniversityOregon Health & Science University
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From Text to Pictures

eagle-i ID

http://ohsu.eagle-i.net/i/0000012c-3ca6-82fe-cc1a-f59980000000

Resource Type

  1. Software

Properties

  1. Related grant number
    NSF Award Abstract #0904361
  2. Resource Description
    This software implements new theoretical models and technology to automatically convert descriptive text into 3D scenes representing the text's meaning. They do this via the Scenario-Based Lexical Knowledge Resource (SBLR), a resource they are creating from existing sources (PropBank, WordNet, FrameNet) and from automated mining of Wikipedia and other un-annotated text. In addition to predicate-argument structure and semantic roles, the SBLR includes necessary roles, typical role fillers, contextual elements, and activity poses which enables analysis of input sentences at a deep level and assembly of appropriate elements from libraries of 3D objects to depict the fuller scene implied by a sentence. For example, "Terry ate breakfast" does not tell us where (kitchen, dining room, restaurant) or what he ate (cereal, doughnut, or rice, umeboshi, and natto). These elements must be supplied from knowledge about typical role fillers appropriate for the information that is specified in the input. Note that the SBLR has a component that varies by cultural context.
  3. Used by
    Center for Spoken Language Understanding
  4. Website(s)
    http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=0904361
  5. Website(s)
    http://www.ohsu.edu/xd/education/schools/school-of-medicine/departments/basic-science-departments/biomedical-engineering/center-for-spoken-language-understanding/from-text-to-pictures.cfm?WT_rank=1
  6. Related Technique
    Computational modeling technique
  7. Developed by
    0000012b-36a0-fcf3-62b8-f47280000000
  8. Software license
    Open source software license
 
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Provenance Metadata About This Resource Record

Copyright © 2016 by the President and Fellows of Harvard College
The eagle-i Consortium is supported by NIH Grant #5U24RR029825-02 / Copyright 2016