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	<title>Comments on: Who Wants To Play &#8220;Jeopardy&#8221;?</title>
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		<title>By: Transparent Text Symposium &#124; The Noisy Channel</title>
		<link>http://thenoisychannel.com/2009/04/27/who-wants-to-play-jeopardy/comment-page-1/#comment-4438</link>
		<dc:creator>Transparent Text Symposium &#124; The Noisy Channel</dc:creator>
		<pubDate>Sat, 19 Sep 2009 17:46:32 +0000</pubDate>
		<guid isPermaLink="false">http://thenoisychannel.com/?p=2023#comment-4438</guid>
		<description>[...] some of its own researchers to the program, including David Ferrucci, who has been leading the Jeopardy project. There&#8217;s even an &#8220;Ignite-style&#8221; session where all attendees will have the [...]</description>
		<content:encoded><![CDATA[<p>[...] some of its own researchers to the program, including David Ferrucci, who has been leading the Jeopardy project. There&#8217;s even an &#8220;Ignite-style&#8221; session where all attendees will have the [...]</p>
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		<title>By: Daniel Tunkelang</title>
		<link>http://thenoisychannel.com/2009/04/27/who-wants-to-play-jeopardy/comment-page-1/#comment-2997</link>
		<dc:creator>Daniel Tunkelang</dc:creator>
		<pubDate>Mon, 27 Apr 2009 15:34:12 +0000</pubDate>
		<guid isPermaLink="false">http://thenoisychannel.com/?p=2023#comment-2997</guid>
		<description>Mark, that&#039;s a good point. Though I suspect the early chess-playing programs made stupid mistakes too, at least from what I read of early evaluation heuristics.

Ellen, you&#039;re right that I may be raising the bar too high. But I&#039;d like, at a minimum, for the system to be able to express the query it believes the offered results answer, so that a user can compare that back-fitted query to his or her information need. The back-fitted query may be different that the original one, but I&#039;d hope the user would be able to make the connection. Or perhaps the serendipity of the connection would itself be useful. In any case, I&#039;d start by setting what I believe is an achievable goal: requiring that search results offer at least one user-understandable query that can be reverse engineered from them. That would be a big step toward transparency.</description>
		<content:encoded><![CDATA[<p>Mark, that&#8217;s a good point. Though I suspect the early chess-playing programs made stupid mistakes too, at least from what I read of early evaluation heuristics.</p>
<p>Ellen, you&#8217;re right that I may be raising the bar too high. But I&#8217;d like, at a minimum, for the system to be able to express the query it believes the offered results answer, so that a user can compare that back-fitted query to his or her information need. The back-fitted query may be different that the original one, but I&#8217;d hope the user would be able to make the connection. Or perhaps the serendipity of the connection would itself be useful. In any case, I&#8217;d start by setting what I believe is an achievable goal: requiring that search results offer at least one user-understandable query that can be reverse engineered from them. That would be a big step toward transparency.</p>
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		<title>By: Ellen Voorhees</title>
		<link>http://thenoisychannel.com/2009/04/27/who-wants-to-play-jeopardy/comment-page-1/#comment-2996</link>
		<dc:creator>Ellen Voorhees</dc:creator>
		<pubDate>Mon, 27 Apr 2009 14:46:34 +0000</pubDate>
		<guid isPermaLink="false">http://thenoisychannel.com/?p=2023#comment-2996</guid>
		<description>People aren&#039;t necessarily very good at guessing the original query from a sampling of the relevant documents.

In the TREC query track, the goal was to get very many different expressions (&quot;queries&quot;) of the same information need (&quot;topic&quot;) for topics that had already been used in an ad hoc task. One of the ways of getting new queries was to show someone unfamiliar with the original topic statement a sampling of five or so relevant documents and have them write a query for which those documents would be relevant.  Some of those back-fitted queries are quite dissimilar from the original topic statement.

Ellen</description>
		<content:encoded><![CDATA[<p>People aren&#8217;t necessarily very good at guessing the original query from a sampling of the relevant documents.</p>
<p>In the TREC query track, the goal was to get very many different expressions (&#8220;queries&#8221;) of the same information need (&#8220;topic&#8221;) for topics that had already been used in an ad hoc task. One of the ways of getting new queries was to show someone unfamiliar with the original topic statement a sampling of five or so relevant documents and have them write a query for which those documents would be relevant.  Some of those back-fitted queries are quite dissimilar from the original topic statement.</p>
<p>Ellen</p>
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		<title>By: Mark Johnson</title>
		<link>http://thenoisychannel.com/2009/04/27/who-wants-to-play-jeopardy/comment-page-1/#comment-2995</link>
		<dc:creator>Mark Johnson</dc:creator>
		<pubDate>Mon, 27 Apr 2009 04:28:46 +0000</pubDate>
		<guid isPermaLink="false">http://thenoisychannel.com/?p=2023#comment-2995</guid>
		<description>Thanks for alerting us to this.  Very interesting!  Ambiguity, double entendre, puns, and anologies are fascinating, but regular question answering systems still suck pretty hardcore.  I wonder if research on the former will help the latter?

BTW, one of the things I find most interesting about these systems (unlike chess-playing programs) is that they make mistakes that humans would think are completely ridiculous.  I think success in this space shouldn&#039;t just be defined by beating humans, but by not making inane mistakes (e.g., when a question clearly calls for a thing of class X, the system gives an answer in class Y).</description>
		<content:encoded><![CDATA[<p>Thanks for alerting us to this.  Very interesting!  Ambiguity, double entendre, puns, and anologies are fascinating, but regular question answering systems still suck pretty hardcore.  I wonder if research on the former will help the latter?</p>
<p>BTW, one of the things I find most interesting about these systems (unlike chess-playing programs) is that they make mistakes that humans would think are completely ridiculous.  I think success in this space shouldn&#8217;t just be defined by beating humans, but by not making inane mistakes (e.g., when a question clearly calls for a thing of class X, the system gives an answer in class Y).</p>
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