  Determining the attachments of prepositions and subordinate conjunctions is a
key problem in parsing natural language. This paper presents a trainable
approach to making these attachments through transformation sequences and
error-driven learning. Our approach is broad coverage, and accounts for roughly
three times the attachment cases that have previously been handled by
corpus-based techniques. In addition, our approach is based on a simplified
model of syntax that is more consistent with the practice in current
state-of-the-art language processing systems. This paper sketches syntactic and
algorithmic details, and presents experimental results on data sets derived
from the Penn Treebank. We obtain an attachment accuracy of 75.4% for the
general case, the first such corpus-based result to be reported. For the
restricted cases previously studied with corpus-based methods, our approach
yields an accuracy comparable to current work (83.1%).
