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Dissertation Information


Title: Productivity in Argument Selection: A usage-based approach to lexical choice in syntactic slots Add Dissertation
Author: Amir Zeldes Update Dissertation
Email: click here to access email
Homepage: http://u.hu-berlin.de/zeldes
Degree Awarded: Humboldt-Universit├Ąt zu Berlin , Department for German Language and Lingustics
Completed in:
2011
Linguistic Subfield(s): Linguistic Theories Morphology Syntax Text/Corpus Linguistics
Director(s): Stefan Gries
Anke L├╝deling

Abstract: The process of argument selection, while seemingly completely productive,
is restricted in actual usage. Though we cannot enumerate all possible
objects of a verb like drink or eat, it's easy to observe empirically that
arguments of drink are fewer, more repetitive and less prone to innovation
than those of eat in virtually any sample of usage data. Once a certain
sample size has been reached, it becomes difficult to observe novel
material for any argument selection process, but how quickly this point is
reached differs in different constructions. Though there are clear
pragmatic reasons why this may be, some cases seem arbitrary. Why do
speakers of English seem to shake so many different things but jog so few
(other than [someone's] memory)? Why should near synonyms like
start/begin/commence exhibit a significantly different likelihood of
admitting novel objects? What makes speakers generate different sized
vocabularies for verbal complements of help to [VERB] vs. help [VERB]? This
thesis is dedicated to answering such questions by positing productivity, a
concept developed for morphological word formation, as the quantifiable
property responsible for these differences, and determining its theoretical
status in syntactic argument selection in two ways.

First, I will show that speakers have implicit knowledge of how productive
different constructions are. The sense of productivity meant here comprises
multiple dimensions: most central is the likelihood to produce novel
regular forms, a quantity estimated in the sense of Baayen's (1993)
Potential Productivity in morphology. However other aspects of what I call
the 'Productivity Complex' (PC) are closely related, with attested and
projected vocabulary sizes also playing an important role. By comparing
these properties of constructions, I propose a multidimensional model of
productivity in syntax. My main line of argumentation will show that: 1.
empirical productivity estimates for the same construction behave
consistently within and across datasets; 2. that for many reasons,
productivity cannot be accounted for on semantic grounds without resorting
to 'per head semantic classes' (criticized by Dowty 1991), not least of
which because (near) synonymous constructions and lexical heads have
idiosyncratic productive behavior; and 3. many partially filled exponents
of productive patterns have a saturated vocabulary which must be stored in
the mental lexicon to account for the data. Knowledge of productivity
complements speakers' knowledge of preferred arguments (cf.
Stefanowitsch/Gries 2003), and explains differential generation of novel
forms.

Second, I will attempt to integrate findings into a theory of how
productivity is acquired from input. Work in Construction Grammar has shown
how skewed distributional properties are more readily acquired by both
children and adults (Casenhiser/Goldberg 2005). Constructions with few very
frequent types and many infrequent ones (a typical LNRE distribution,
Baayen 2001) are acquired more quickly and extended more easily by
speakers. In line with these findings, I show that productive syntactic
constructions exhibit these properties and are acquired for productive use
by speakers, who reproduce a similar distribution, possibly feeding into
diachronic developments in productivity. Finally, since productivity is
linked to regularity, I suggest that 'being a rule of grammar' is in fact a
scalar property, corresponding to the extensibility of a construction as
predicted by the PC model which emerges from its representation in the
mental lexicon.