A U-DOP approach to modeling language acquisition
Margaux Smets

Abstract:
In linguistics, there is a debate between empiricists and nativists:
the former believe that language is acquired from experience, the
latter that there is an innate component for language. The main
arguments adduced by nativists are Arguments from Poverty of
Stimulus. It is claimed that children acquire certain phenomena, which
they cannot learn on the basis of experience alone ---and therefore,
there has to be some innate component for language. In this thesis, we
show that at least for certain phenomena that are often used in such
arguments, it is possible to explain how children acquire them on the
basis of experience alone, viz. with an Unsupervised Data-Oriented
Parsing (U-DOP) approach to language.

In the first part of the thesis, we develop concrete implementations
of U-DOP, and contribute to the field of unsupervised parsing with two
innovations. First, we develop an algorithm that performs syntactic
category labeling and parsing simultaneously, and second, we devise a
new methodology for unsupervised parsing, which can in principle be
applied to any unsupervised parsing algorithm, and which produces the
best results reported on the ATIS-corpus so far, with a promising
outlook for even better results.

In the second part of the thesis, we then use these concrete
implementations to show how the acquisition of certain phenomena can
be explained in an empirical way. We look in detail at wh-questions,
and then show that the U-DOP approach is more general than the
nativist account by looking at other phenomena.