Users of natural languages have many word orders with which to
encode the same truth-conditional meaning. They choose contextually
appropriate strings from these many ways with little conscious
effort and with effective communicative results. Previous
computational models of when English speakers produce non-canonical
word orders, like topicalization, left-dislocation, and clefts,
fail-either by overgenerating these statistically rare forms or by
undergenerating. The primary goal of this book is to present a
better model of when speakers choose to produce certain
non-canonical word orders by incorporating the effects of discourse
context and speaker goals on syntactic choice. The theoretical
model is then used as a basis for building a probabilistic
classifier that can select the most human-like word order based on
the surrounding discourse context. The model of discourse context
used is a methodological advance both from a theoretical and an
engineering perspective. It is built up from individual linguistic
features, ones more easily and reliably annotated than the direct
annotation of a discourse or rhetorical structure for a text. This
book makes extensive use of previously unexamined naturally
occurring corpus data of non-canonical word order in English, both
to illustrate the points of the theoretical model and to train the
statistical model.
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