@inproceedings{2941cd011dca45a4b9e349da62a68ad1,
title = "A neural network for coordination boundary prediction",
abstract = "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.",
author = "Jessica Ficler and Yoav Goldberg",
note = "Publisher Copyright: {\textcopyright} 2016 Association for Computational Linguistics; 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016 ; Conference date: 01-11-2016 Through 05-11-2016",
year = "2016",
month = jan,
day = "1",
doi = "10.18653/v1/d16-1003",
language = "الإنجليزيّة",
series = "EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings",
publisher = "Association for Computational Linguistics (ACL)",
pages = "23--32",
booktitle = "EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings",
address = "الولايات المتّحدة",
}