A Neural Network for Coordination Boundary Prediction

2016-10-13 · EMNLP 2016 11 · Jessica Ficler, Yoav Goldberg

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.

📄 PDF Abstract BibTeX arXiv:1610.03946

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