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    <title>BestTechVideos: Videos Tagged with 'adaptor'</title>
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    <pubDate>Thu, 27 Oct 2011 21:12:16 GMT</pubDate>
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      <title>Learning Rules with Adaptor Grammars</title>
      <link>http://www.bestechvideos.com/2009/07/17/learning-rules-with-adaptor-grammars</link>
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        <![CDATA[<table cellspacing="0" cellpadding="0" border="0"><tr><td width="170" valign="top"><p><a href="http://www.bestechvideos.com/2009/07/17/learning-rules-with-adaptor-grammars"><img alt="Learning Rules with Adaptor Grammars" src="/uploads/btv/video/image/00000001/9734/thumb_large_image.jpg" style="border: 1px solid #7f7f7f;" /></a></p></td><td valign="top">[note: apologies for the overscanned slides - you can find full resolution slides at http://www.cog.brown.edu/~mj/papers/johnson09-learning-rules-g.pdf ]<br /><br />Presented by Mark Johnson, Brown University.<br /><br />Nonparametric Bayesian methods are interesting because they may provide a way of learning the appropriate units of generalization as well as the generalization's probability or weight.  Adaptor Grammars are a framework for stating a variety of hierarchical nonparametric Bayesian models, where the units of generalization can be viewed as<br />kinds of PCFG rules.  This talk describes the mathematical and computational properties of Adaptor Grammars and linguistic applications such as word segmentation and syllabification,<br />and describes the MCMC algorithms we use to sample them.<br /><br />Joint work with Sharon Goldwater and Tom Griffiths.<p><em>Google Tech Talks<br />July 6, 2009</em></p><p><a href="http://www.bestechvideos.com/2009/07/17/learning-rules-with-adaptor-grammars">Read more about this video…</a></p></td></tr></table><hr/>Want more on these topics?<br/>Browse the archive of posts filed under <a href="http://www.bestechvideos.com/category/conferences">Conferences</a>, <a href="http://www.bestechvideos.com/category/companies">Companies</a>]]>
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      <pubDate>Thu, 27 Oct 2011 21:12:16 GMT</pubDate>
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      <comments>http://www.bestechvideos.com/2009/07/17/learning-rules-with-adaptor-grammars</comments>
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