MODELING SYNTAX for PARSING and TRANSLATION
Peter Venable, December 2003
PhD Thesis, Carnegie Mellon School of Computer Science

ABSTRACT

Syntactic structure is an important component of natural language
utterances, for both form and content.  Therefore, a variety of
applications can benefit from the integration of syntax into their
statistical models of language.  In this thesis, two new syntax-based
models are presented, along with their training algorithms: a
monolingual generative model of sentence structure, and a model of the
relationship between the structure of a sentence in one language and
the structure of its translation into another language.  After these
models are trained and tested on the respective tasks of monolingual
parsing and word-level bilingual corpus alignment, they are
demonstrated in two additional applications.  First, a new statistical
parser is automatically induced for a language in which none was
available, using a bilingual corpus.  Second, a statistical
translation system is augmented with syntax-based models.  Thus the
contributions of this thesis include: a statistical parsing system; a
bilingual parsing system, which infers a structural relationship
between two languages using a bilingual corpus; a method for
automatically building a parser for a language where no parser is
available; and a translation model that incorporates phrase structure.