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A Deep Neural Network Sentence Level Classification Method with Context Information

Research output: Chapter in Book/Report/Conference proceedingConference paper

Xingyi Song, Johann Petrak, Angus Roberts

Original languageEnglish
Title of host publicationProceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
PublisherAssociation for Computational Linguistics
Pages900-904
Number of pages5
ISBN (Print)978-1-948087-84-1
Publication statusPublished - 2 Nov 2018

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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.

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