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.
Original language | English |
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Title of host publication | EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 23-32 |
Number of pages | 10 |
ISBN (Electronic) | 9781945626258 |
DOIs | |
State | Published - 2016 |
Event | 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016 - Austin, United States Duration: 1 Nov 2016 → 5 Nov 2016 |
Publication series
Name | EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings |
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Conference
Conference | 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016 |
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Country/Territory | United States |
City | Austin |
Period | 1/11/16 → 5/11/16 |
Bibliographical note
Publisher Copyright:© 2016 Association for Computational Linguistics
Funding
This work was supported by The Israeli Science Foundation (grant number 1555/15) as well as the German Research Foundation via the German-Israeli Project Cooperation (DIP, grant DA 1600/1-1).
Funders | Funder number |
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DIP | DA 1600/1-1 |
German-Israeli Project Cooperation | |
Israeli Science Foundation | 1555/15 |
The Israeli Science Foundation | |
Deutsche Forschungsgemeinschaft | |
German-Israeli Foundation for Scientific Research and Development |