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<DIV><FONT size=2>Hey all,</FONT></DIV>
<DIV><FONT size=2></FONT> </DIV>
<DIV><FONT size=2>Just an FYI for anybody who took Roger's class last quarter
but isn't on this email list... an opportunity to squeeze one more lecture out
of our intrepid leader :)</FONT></DIV>
<DIV><FONT size=2></FONT> </DIV>
<DIV><FONT size=2>Ben</FONT></DIV>
<DIV style="FONT: 10pt arial">----- Original Message -----
<DIV style="BACKGROUND: #e4e4e4; font-color: black"><B>From:</B> <A
title=aborovsk@crl.ucsd.edu href="mailto:aborovsk@crl.ucsd.edu">Arielle
Borovsky</A> </DIV>
<DIV><B>To:</B> <A title=talks@crl.ucsd.edu
href="mailto:talks@crl.ucsd.edu">talks@crl.ucsd.edu</A> </DIV>
<DIV><B>Sent:</B> Saturday, April 05, 2008 4:29 PM</DIV>
<DIV><B>Subject:</B> CRL talk 4/8: Roger Levy</DIV></DIV>
<DIV><FONT size=2></FONT><BR></DIV>
<DIV
style="TEXT-ALIGN: center">*********************************************<BR></DIV>
<DIV style="TEXT-ALIGN: center"><SPAN><SPAN><SPAN
class=nfakPe>CRL</SPAN></SPAN></SPAN> Happy Half Hour @ 3:30 in CSB
215<SPAN><SPAN></SPAN></SPAN><BR><SPAN><SPAN><SPAN
class=nfakPe>CRL</SPAN></SPAN></SPAN> <SPAN><SPAN><SPAN
class=nfakPe>Talk</SPAN></SPAN></SPAN> by Roger Levy @ 4 in CSB 280<BR></DIV>
<DIV
style="TEXT-ALIGN: center">*********************************************</DIV>
<P style="TEXT-ALIGN: center"><FONT size=4><B>Roger Levy<BR></B></FONT></P>
<P style="TEXT-ALIGN: center"><FONT size=4><B><I>Modeling uncertainty about the
input in online sentence comprehension </I><SPAN> </SPAN></B></FONT></P>
<P>Nearly every aspect of language processing is evidential---that is, it
requires informed yet uncertain judgment on the part of the processor. To the
extent that language processing is probabilistic, this means that a rational
processing strategy could in principle attend to information from disparate
sources (lexical, syntactic, discourse context, background world knowledge,
visual environment) to optimize rapid belief formation---and there is evidence
that information from many of these sources is indeed brought to bear in
incremental sentence comprehension (e.g., MacDonald, 1993; Frazier & Rayner,
1982; Rohde et al., 2008; McRae et al., 2005; Tanenhaus et al., 1995).
Nevertheless, nearly all formalized models of online sentence comprehension
implicitly contain an important interface constraint that limits the use of
cross-source information in belief formation: namely, the "input" to the
sentence processor consists of a sequence of words, whereas a more natural
representation would be something like the output of a word-recognition
model---a probability distribution over word sequences. In this talk, I
examine how online sentence comprehension might be formalized if this constraint
is relaxed. I show how generative probabilistic grammars can be a unifying
framework for representing both this type of uncertain input and the
probabilistic grammatical information constituting a comprehender's knowledge of
their own language. The outcome of the comprehension process is then
simply the intersection of a probabilistic input with a probabilistic grammar.
I then show how this model may shed light on two outstanding puzzles in
the sentence comprehension literature: (i) data underlying the "good enough
representation" approach of (F.) Ferreira et al. (2003), such as (1) below:
<SPAN> </SPAN> </P>
<P>While Anna dressed the baby spit up in the bed. <SPAN> </SPAN></P>
<P>where "the baby" is taken by many readers to be both the theme of "dressed"
and the agent of "spit up", and (ii) the local-coherence effects of Tabor et al.
(2004), in which sentences such as (2) below:
<SPAN> </SPAN> <BR> <BR>The coach smiled at the player tossed the
frisbee. <SPAN> </SPAN></P>
<P>elicit what are apparently classic garden-path effects despite the fact that
global context seemingly should rule out the garden path before it is every
pursued.<SPAN> </SPAN></P>
<P>
<HR>
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