PP Attachment: Where do We Stand?
Daniël De Kok, Jianqiang Ma, Corina Dima, Erhard Hinrichs · 2017
Prepositional phrase (PP) attachment is a well known challenge to parsing.In this paper, we combine the insights of different works, namely: (1) treating PP attachment as a classification task with an arbitrary number of attachment candidates;(2) using auxiliary distributions to augment the data beyond the hand-annotated training set; (3) using topological fields to get information about the distribution of PP attachment throughout clauses and (4) using state-of-the-art techniques such as word embeddings and neural networks.We show that jointly using these techniques leads to substantial improvements.We also conduct a qualitative analysis to gauge where the ceiling of the task is in a realistic setup.