[OPLIN 4cast] OPLIN 4Cast #243: Natural language Question Answering

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OPLIN 4Cast

OPLIN 4Cast #243: Natural language Question Answering
August 17th, 2011

<http://www.oplin.org/4cast/wp-content/uploads/2011/08/smart_computer1.png>An 
article 
<http://www.readwriteweb.com/enterprise/2011/08/gartner-adds-big-data-gamifica.php> 
caught our attention last week concerning some additions to Gartner 
Research's Hype Cycle for Emerging Technologies. If you're not familiar 
with the Hype Cycles, the Garner website has a good explanation 
<http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp>, 
but what they basically do is predict the life cycle of technologies as 
they move through inflated expectations to eventual productivity. One of 
the emerging technologies in the current hype cycle is natural language 
Question Answering (QA), which Gartner predicts will reach mainstream 
adoption in 5-10 years. For libraries, this is reminiscent of the plot 
of that old movie Desk Set <http://www.imdb.com/title/tt0050307/>; will 
computers - similar to the IBM Watson system that recently competed on 
/Jeopardy!/ - soon be replacing reference librarians?

    * How does QA technology compare to document search?
      <http://www.research.ibm.com/deepqa/faq.shtml#22> (IBM DeepQA
      Project FAQ) "The key difference between QA technology and
      document search is that document search takes a keyword query and
      returns a list of documents, ranked in order of relevance to the
      query, while QA technology takes a question expressed in natural
      language, seeks to understand it in much greater detail, and
      returns a precise answer to the question."
    * What is artificial intelligence?
      <http://www.nytimes.com/2011/02/06/opinion/06powers.html> (New
      York Times Opinion/Richard Powers) "Open-domain question answering
      has long been one of the great holy grails of artificial
      intelligence.[...] It goes well beyond what search engines like
      Google do when they comb data for keywords. Google can give you
      300,000 page matches for a search of the terms 'greyhound,'
      'origin' and 'African country,' which you can then comb through at
      your leisure to find what you need. Asked in what African country
      the greyhound originated, Watson can tell you in a couple of
      seconds that the authoritative consensus favors Egypt."
    * Katz explains contributions to Watson /Jeopardy!/ challenge
      <http://www.csail.mit.edu/node/1424> (MIT CSAIL News/Abby
      Abazorius) "[Principal Research Scientist Boris] Katz's model of
      syntactic decomposition helps Watson decipher complex,
      multi-pronged questions by allowing the system to understand that
      it needs to tackle several sub-questions. The system then uses an
      algorithm that helps it decide which sub-questions to answer and
      in what order, and compiles the gathered information into a
      cohesive, and hopefully correct, answer."
    * An analysis of the AskMSR question-answering system
      <http://research.microsoft.com/en-us/um/people/sdumais/EMNLP_Final.pdf>
      (Microsoft Research/Eric Brill et. al.) [pdf] "Typically, when
      deploying a question answering system, there is some cost
      associated with returning incorrect answers to a user. Therefore,
      it is important that a QA system has some idea as to how likely an
      answer is to be correct, so it can choose not to answer rather
      than answer incorrectly.[...] Ideally, we would like to be able to
      determine the likelihood of answering correctly solely from an
      analysis of the question."

*/Watson fact:/*

The IBM Watson computer system used on /Jeopardy!/ had 200 million pages 
of information stored in its memory, including the full text of Wikipedia.
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