Template Kernels for Dependency Parsing
Hillel Taub-Tabib, Yoav Goldberg, Amir Globerson · 2015
A common approach to dependency parsing is scoring a parse via a linear function of a set of indicator features.These features are typically manually constructed from templates that are applied to parts of the parse tree.The templates define which properties of a part should combine to create features.Existing approaches consider only a small subset of the possible combinations, due to statistical and computational efficiency considerations.In this work we present a novel kernel which facilitates efficient parsing with feature representations corresponding to a much larger set of combinations.We integrate the kernel into a parse reranking system and demonstrate its effectiveness on four languages from the CoNLL-X shared task. 1