Sieve-based spatial relation extraction with expanding parse trees

Abstract

A key challenge introduced by the recent SpaceEval shared task on spatial relation extraction is the identification of MOVELINKS, a type of spatial relation in which up to eight spatial elements can participate. To handle the complexity of extracting MOVELINKs, we combine two ideas that have been successfully applied to information extraction tasks, namely tree kernels and multi-pass sieves, proposing the use of an expanding parse tree as a novel structured feature for training MOVELINK classifiers. Our approach yields state-of-the-art results on two key tasks in SpaceEval.