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Mining the Web for Multimedia Question Answering. Yiming Yang Language Technologies Institute & Computer Science Department Carnegie Mellon University. Research Team. Yiming Yang (PI) Jaime Carbonell (Co-PI) Yan Liu (PhD student) Bora Cenk Gazen (PhD student) Fan Li (PhD student)

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mining the web for multimedia question answering

Mining the Web forMultimedia Question Answering

Yiming Yang

Language Technologies Institute &

Computer Science Department

Carnegie Mellon University

research team
Research Team
  • Yiming Yang (PI)
  • Jaime Carbonell (Co-PI)
  • Yan Liu (PhD student)
  • Bora Cenk Gazen (PhD student)
  • Fan Li (PhD student)
  • Shoubin Dong (Visiting researcher)
a picture is worth a thousand of words
A picture is worth a thousand of words

Example 1. Student demographics

  • What are the race distribution of the international students in university X?
  • Is there an unexpected trend in the admission?
  • Do other universities have a similar trend recently?
example 2 macro economics
Example 2. Macro-economics
  • What is the overall trend in the balance of trade between the US and mainland China?
  • Do similar cases exist in recent history, and if so, between which countries?
  • Is there any unexpected trend in trading agricultural products in Asian/Pacific region?
technical components
Technical Components
  • Templates defined for Q/A of certain types
  • Web crawling tools
    • Text categorization for relevant pages
    • HTML/XML parsing for tables & images
  • Information extraction techniques
    • Named Entities, tables, graphs & text
    • Templates connecting multi-media objects
  • Q/A mapping and reasoning
    • Collaborative filtering of frequent Q’s and A’s
    • Case-based reasoning across text, tables & graphs
    • Numerical analysis of graphical similarity (lines)
hypotheses
Hypotheses
  • Answers combining text and pictures are beneficial for certain types of questions.
  • Valuable information in multi-media forms

can be gathered from the Web.

  • Information extraction & comparative image analysis (graphs, curves) can be jointly used for cross-media data mining.
  • Template-based reasoning can serve as a vehicle for multi-media Q/A.
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