Mining the web for multimedia question answering
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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

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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?


Tends in international student admission

Tends in international student admission


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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