Coordination is an important and common syntactic construction which is not handled well by state of the art parsers.Coordinations in the Penn Treebank are missing internal structure in many cases, do not include explicit marking of the conjuncts and contain various errors and inconsistencies.In this work, we initiated manual annotation process for solving these issues.We identify the different elements in a coordination phrase and label each element with its function.We add phrase boundaries when these are missing, unify inconsistencies, and fix errors.The outcome is an extension of the PTB that includes consistent and detailed structures for coordinations.We make the coordination annotation publicly available, in hope that they will facilitate further research into coordination disambiguation. 1

We propose a neural-network based model for coordination boundary prediction. The network is designed to incorporate two signals: the similarity between conjuncts and the observation that replacing the whole coordination phrase with a conjunct tends to produce a coherent sentences. The modeling makes use of several LSTM networks. The model is trained solely on conjunction annotations in a Treebank, without using external resources. We show improvements on predicting coordination boundaries on the PTB compared to two state-of-the-art parsers; as well as improvement over previous coordination boundary prediction systems on the Genia corpus.