  The goal of this thesis is to advance the exploration of the statistical
language learning design space. In pursuit of that goal, the thesis makes two
main theoretical contributions: (i) it identifies a new class of designs by
specifying an architecture for natural language analysis in which probabilities
are given to semantic forms rather than to more superficial linguistic
elements; and (ii) it explores the development of a mathematical theory to
predict the expected accuracy of statistical language learning systems in terms
of the volume of data used to train them.
  The theoretical work is illustrated by applying statistical language learning
designs to the analysis of noun compounds. Both syntactic and semantic analysis
of noun compounds are attempted using the proposed architecture. Empirical
comparisons demonstrate that the proposed syntactic model is significantly
better than those previously suggested, approaching the performance of human
judges on the same task, and that the proposed semantic model, the first
statistical approach to this problem, exhibits significantly better accuracy
than the baseline strategy. These results suggest that the new class of designs
identified is a promising one. The experiments also serve to highlight the need
for a widely applicable theory of data requirements.
