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

Chapter 1 Preliminaries
rial, in Levy’s approach the structural probabilities of the grammar are perfectly known
to the parser. Consequently, the parallel probabilistic resource allocation theory by Levy
constitutes as sort of competence model with surprisal or relative entropy as a “bottleneck”
to comprehension, thus yielding performance-related predictions. Predictions of
frequency-based approaches like the tuning hypothesis (Mitchell, Cuetos, Corley, and
Brysbaert, 1995) are quite similar to surprisal most of the time but differ fundamentally
in head-final structures. Similarly DLT and surprisal make comparable predictions only
in structures that are not head-final. In head-final constructions the preceding dependents
provide statistical information about the nature of the head, thus narrowing the
prediction. Following the theory, a better prediction (or lower surprisal) facilitates integration
on the head. Thus an expectation-based theory predicts language-independent
anti-locality effects in head-final structures.
1.3.3 Canonicity
In literature the term canonicity with respect to word order is often used as synonymous
to regularity and structural frequency. Here these terms shall be distinguished in order
to clearly formulate respective theories.
A theory of canonicity has to answer two questions:
1. What categorial domain is the focus of the canonicity?
2. What makes specific structures canonical?
The categorial focus of canonicity can be grammatical functions, thematic roles, letter
sequences, prosody and the like. The specific structures counting as canonical in these
domains can be chosen by structural regularity, complexity, or simply by convention.
The most common canonicity account goes back to Greenberg (1963) and relates to
the basic grammatical functions subject, object, and predicate and is justified by structural
regularities. Greenberg classified languages in terms of their canonical word order.
He and subsequent literature count English as a subject-verb-object (SVO) language because
simple sentences and most subordinate constructions follow that order. Therewith
it belongs to the second biggest class (41.79%) preceded by the SOV order attributed
to 44.78% (according to Tomlin, 1986) of the languages of the world. However, the
classification is not as clear for all languages. German is arguably an SOV language
although the simplest sentence structure is built with an SVO order like English. For
example, Erdmann (1990) concludes that German does not fulfill all requirements for
an SOV language and should therefore be categorized as SVO.
As mentioned above, structural regularity based on corpus occurences is not the only
possibility to ground a canonicity account on. A generative grammar-based account
that relates word order canonicity to language processing assumes the language-specific
canonical structure as an internal representation underlying the surface structure (Lin
et al., 2005). Thus, in order to comprehend a non-canonically structured sentence the
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