Connectionist Modeling of Experience-based Effects in Sentence ...

Chapter 1 Preliminaries
are limited by the same constraints. An experience-based account clearly favors the
former explanation, which, of course, does not exclude the second possibility but does
not depend on it.
The Structural Grain Size
A serious problem for the formulation of symbolic theories of handling exposure-based
parsing decisions is the question of grain size. As in canonicity accounts the question to
answer is on which structural level information should be considered as affecting parsing
decisions. A symbolic exposure-based account like the one by Mitchell, Cuetos, Corley,
and Brysbaert (1995) tabulates the frequencies of specific structures. For each relevant
structure there is a table listing its different interpretations (e.g. attachment site
in complex noun phrases). When the parser processes an ambiguous construction the
most frequent of the relevant recorded structures (frames or partial syntactic representations)
is merged with the current sentence structure to yield a predicted disambiguated
structure.
“The success of this process depends upon establishing a useful link between
aspects of the current material and corresponding features of the established
records. This is essentially a category selection or pattern-matching
problem.” (p. 470).
The recorded structures can be specified in deep detail, say, on a lexical level, or rather
abstracted, e.g., on the level of phrasal categories. Example (5) (Mitchell et al., 1995)
contains a global ambiguity. In (5a) the RC who was outside the house can be attached
to either the first noun wife or the second noun football star. The same is true for the
PP outside the house in (5b).
(5) a. Someone stabbed the wife of the football star who was outside the house.
b. Someone stabbed the estranged wife of the movie star outside the house.
In an exposure-based account the parser’s decision about noun one (high) or noun two
(low) attachment depends on the corpus frequencies of both possibilities. These frequencies
could be calculated on several possible structural levels. For example frequencies
could be tabulated individually for each construction by tabulating attachment preferences
for NP-PP-RC structures as well as for NP-PP-PP structures. Also, the preferences
could be tabulated for both constructions pooled together by recording the occurrences of
the more abstracted NP-PP-(modifying constituent) structure. The choice of grain size
crucially affects the theory’s predictions. A too fine grained record level is in danger of
missing out some affected constructions. A very abstract level on the other hand can lead
to overgeneralization. Mitchel, Cuetos et al. categorize existing exposure-based models
into a) fine-grained (Spivey-Knowlton, 1994), b) coarse-grained (Cuetos et al.,
1996), and c) mixed-grain models. Connectionist network models like MacDonald
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