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<p><!-- Make sure you modify the 4Cast title in this section -->
<span style="font-size: 20px; font-weight: bold;
color: rgb(0, 0, 0); font-family: arial;
line-height: 110%;">OPLIN 4Cast #243: Natural
language Question Answering</span><br>
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<span style="font-size: 11px; font-weight: normal;
color: rgb(102, 102, 102); font-style: italic;
font-family: arial;">August 17th, 2011</span></p>
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<p style="text-align: justify; font-size: 16px;
font-family: arial; line-height: 110%;"><a
href="http://www.oplin.org/4cast/wp-content/uploads/2011/08/smart_computer1.png"><img
class="alignleft size-full wp-image-2134"
title="smart computer"
src="cid:part2.04080505.08030901@oplin.org"
alt="" height="112" width="93"></a>An <a
href="http://www.readwriteweb.com/enterprise/2011/08/gartner-adds-big-data-gamifica.php">article</a>
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 <a
href="http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp">good
explanation</a>, 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 <a
href="http://www.imdb.com/title/tt0050307/">Desk
Set</a>; will computers - similar to the IBM
Watson system that recently competed on <em>Jeopardy!</em>
- soon be replacing reference librarians?
</p>
<div> </div>
<ul style="text-align: left;">
<li style="text-align: justify; font-size: 16px;
font-family: arial; line-height: 110%;"><a
href="http://www.research.ibm.com/deepqa/faq.shtml#22">How
does QA technology compare to document search?</a>
(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."</li>
<li style="text-align: justify; font-size: 16px;
font-family: arial; line-height: 110%;"><a
href="http://www.nytimes.com/2011/02/06/opinion/06powers.html">What
is artificial intelligence?</a> (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."</li>
<li style="text-align: justify; font-size: 16px;
font-family: arial; line-height: 110%;"><a
href="http://www.csail.mit.edu/node/1424">Katz
explains contributions to Watson <em>Jeopardy!</em>
challenge</a> (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."</li>
<li style="text-align: justify; font-size: 16px;
font-family: arial; line-height: 110%;"><a
href="http://research.microsoft.com/en-us/um/people/sdumais/EMNLP_Final.pdf">An
analysis of the AskMSR question-answering
system</a> (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."</li>
</ul>
<div style="text-align: left;"> </div>
<p style="text-align: left; font-size: 20px;
font-family: arial; line-height: 110%;"><small><strong><em>Watson
fact:</em></strong></small><br>
</p>
<div style="text-align: justify; font-size: 16px;
font-family: arial; line-height: 110%;">The IBM
Watson computer system used on <em>Jeopardy!</em>
had 200 million pages of information stored in its
memory, including the full text of Wikipedia.
</div>
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