IR-776: (2010) Wu, X. and Smith, D., "Right-branching tree transformation for eager dependency parsing," CIIR Technical Report. [View bibtex]

This paper presents a reversible transformation on dependency trees that produces purely right-branching structures. On their own, these structures are easier for stack-based shift-reduce parsers to learn. We describe experiments training parsers on the transformed trees, parsing raw text, and then reversing the transformation on the results to evaluate. While these conditions do not significantly change parsing accuracy, a parser tailored to exploit the greater predictability of the transformed trees is able to run ten times faster than the widely-used MaltParser system, while using the same modeling and learning infrastructure. We conclude with experiments that explore further search strategies enabled by the transformed structures.