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        <title>A Deep Neural Network Sentence Level Classification Method with Context Information</title>
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        <namePart type="given">Xingyi</namePart>
        <namePart type="family">Song</namePart>
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    <name type="personal">
        <namePart type="given">Angus</namePart>
        <namePart type="family">Roberts</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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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.</abstract>
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%0 Conference Proceedings
%T A Deep Neural Network Sentence Level Classification Method with Context Information
%A Song, Xingyi
%A Petrak, Johann
%A Roberts, Angus
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F song-etal-2018-deep
%X 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.
%R 10.18653/v1/D18-1107
%U https://aclanthology.org/D18-1107
%U https://doi.org/10.18653/v1/D18-1107
%P 900-904

Markdown (Informal)

[A Deep Neural Network Sentence Level Classification Method with Context Information](https://aclanthology.org/D18-1107) (Song et al., EMNLP 2018)

• A Deep Neural Network Sentence Level Classification Method with Context Information (Song et al., EMNLP 2018)

ACL

• Xingyi Song, Johann Petrak, and Angus Roberts. 2018. A Deep Neural Network Sentence Level Classification Method with Context Information. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 900–904, Brussels, Belgium. Association for Computational Linguistics.