  Obtaining syntactic parses is a crucial part of many NLP pipelines. However,
most of the world's languages do not have large amounts of syntactically
annotated corpora available for building parsers. Syntactic projection
techniques attempt to address this issue by using parallel corpora consisting
of resource-poor and resource-rich language pairs, taking advantage of a parser
for the resource-rich language and word alignment between the languages to
project the parses onto the data for the resource-poor language. These
projection methods can suffer, however, when the two languages are divergent.
In this paper, we investigate the possibility of using small, parallel,
annotated corpora to automatically detect divergent structural patterns between
two languages. These patterns can then be used to improve structural projection
algorithms, allowing for better performing NLP tools for resource-poor
languages, in particular those that may not have large amounts of annotated
data necessary for traditional, fully-supervised methods. While this detection
process is not exhaustive, we demonstrate that common patterns of divergence
can be identified automatically without prior knowledge of a given language
pair, and the patterns can be used to improve performance of projection
algorithms.
