18.3602, Diss: Cog Sci/Psycholing/Syntax: Jaeger: 'Redundancy and Syntactic...'

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LINGUIST List: Vol-18-3602. Mon Dec 03 2007. ISSN: 1068 - 4875.

Subject: 18.3602, Diss: Cog Sci/Psycholing/Syntax: Jaeger: 'Redundancy and Syntactic...'

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1)
Date: 30-Nov-2007
From: Tim Jaeger < tiflo at csli.stanford.edu >
Subject: Redundancy and Syntactic Reduction in Spontaneous Speech

 

	
-------------------------Message 1 ---------------------------------- 
Date: Mon, 03 Dec 2007 10:35:31
From: Tim Jaeger [tiflo at csli.stanford.edu]
Subject: Redundancy and Syntactic Reduction in Spontaneous Speech
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Institution: Stanford University 
Program: Department of Linguistics 
Dissertation Status: Completed 
Degree Date: 2006 

Author: Tim Florian Jaeger

Dissertation Title: Redundancy and Syntactic Reduction in Spontaneous Speech 

Dissertation URL:  http://www.bcs.rochester.edu/people/fjaeger/

Linguistic Field(s): Cognitive Science
                     Psycholinguistics
                     Syntax

Subject Language(s): English (eng)


Dissertation Director(s):
Joan Bresnan
Edward A.F. Gibson
Daniel Jurafsky
Elizabeth Closs Traugott
Thomas Wasow

Dissertation Abstract:

Comprehenders are sensitive to probabilistic distributions of linguistic
events (Garnsey et al., 1997; Kamide et al., 2003; Konieczny, 2000; Staub
and Clifton, 2006, inter alia). Expected words and structures are processed
faster than unexpected ones. This raises the question whether syntactic
production, too, is sensitive to probabilities of upcoming material. This
thesis investigates cases of syntactic reduction, as in "I think (that) the
commercial break is over", where the word that can be omitted. I present
evidence from corpus studies of spontaneous speech that syntactic reduction
is more likely if the reduced constituent is predictable. Modern
statistical regression models are used to guard against common challenges
to corpus-based studies (such as clusters and multicollinearity). 

Taken together with evidence from phonetic reduction (in duration, formant
quality, etc.; see, e.g., Aylett and Turk, 2004; Bell et al., 2003; van Son
and Pols, 2003), phoneme omission (e.g. t/d deletion; see Bell et al.,
2003; Gahl and Garnsey, 2004), and argument drop (Resnik, 1996), the
evidence from syntactic reduction supports the Probabilistic Reduction
Hypothesis (PRH): "Where grammar allows it, form reduction is more likely,
the more redundant the information conveyed by the omitted form details is".

The PRH is compatible with production pressure (Ferreira and Dell, 2000,
inter alia) and audience design accounts of reduction (Hawkins, 2004, inter
alia). The results are also compatible with 'uniform information density'
accounts (Aylett, 1999; Aylett and Turk, 2004; Genzel and Charniak, 2002):
speakers may insert or omit that to avoid peaks or troughs in information
density. Uniform information density accounts integrate both production and
audience design accounts, as a uniform amount of information per time/unit
optimizes successful information transfer while minimizing production effort.

The same probabilities that I show to influence production are known to
influence comprehension (Garnsey et al., 1997). Hence, the results may be
taken to argue that language users' representations of constituents contain
probabilistic information. In short, knowledge of grammar may imply
knowledge of probabilities (see Gahl and Garnsey, 2004).

The final part of the thesis presents a new approach to studying what
information speakers use to keep track of probabilistic distributions. In a
first step, several predictability estimates of the same event are derived
using different sets of cues or slightly different assumptions about what
the relevant structure is that speakers keep track of. In a second step,
these different predictability estimates are compared with regard to how
much variation in syntactic reduction they account for. The results suggest
that both structural and surface cues are used to keep track of the
probability of structures and that speakers keep track of rather specific
structures. 





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