Song, X., Petrak, J. orcid.org/0000-0001-8038-3096 and Roberts, A. (2018) A deep neural network sentence level classification method with context information. In: Riloff, E., Chiang, D., Hockenmaier, J. and Tsujii, J., (eds.) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018 Conference on Empirical Methods in Natural Language Processing, 31 Oct - 04 Nov 2018, Brussels, Belgium. Association for Computational Linguistics (ACL) , pp. 900-904. ISBN 9781948087841
Abstract
In the sentence classification task, context formed from sentences adjacent to the sentence being classified can provide important information for classification. This context is, however, often ignored. Where methods do make use of context, only small amounts are considered, making it difficult to scale. We present a new method for sentence classification, Context-LSTM-CNN, that makes use of potentially large contexts. The method also utilizes long-range dependencies within the sentence being classified, using an LSTM, and short-span features, using a stacked CNN. Our experiments demonstrate that this approach consistently improves over previous methods on two different datasets.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2018 Association for Computational Linguistics. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 24 Aug 2020 11:21 |
Last Modified: | 24 Aug 2020 11:21 |
Published Version: | https://www.aclweb.org/anthology/D18-1107 |
Status: | Published |
Publisher: | Association for Computational Linguistics (ACL) |
Refereed: | Yes |
Identification Number: | 10.18653/v1/d18-1107 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:164747 |