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Obtaining syntactic parses is a crucial part of many NLP pipelines. However,most of the world's languages do not have large amounts of syntacticallyannotated corpora available for building parsers. Syntactic projectiontechniques attempt to address this issue by using parallel corpora consistingof resource-poor and resource-rich language pairs, taking advantage of a parserfor the resource-rich language and word alignment between the languages toproject the parses onto the data for the resource-poor language. Theseprojection 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 betweentwo languages. These patterns can then be used to improve structural projectionalgorithms, allowing for better performing NLP tools for resource-poorlanguages, in particular those that may not have large amounts of annotateddata necessary for traditional, fully-supervised methods. While this detectionprocess is not exhaustive, we demonstrate that common patterns of divergencecan be identified automatically without prior knowledge of a given languagepair, and the patterns can be used to improve performance of projectionalgorithms.

Capturing divergence in dependency trees to improve syntactic projection