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        <title>LIA at SemEval-2017 Task 4: An Ensemble of Neural Networks for Sentiment Classification</title>
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    <name type="personal">
        <namePart type="given">Mickael</namePart>
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            <title>Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)</title>
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            <namePart type="given">Steven</namePart>
            <namePart type="family">Bethard</namePart>
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        <name type="personal">
            <namePart type="given">Marine</namePart>
            <namePart type="family">Carpuat</namePart>
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        <name type="personal">
            <namePart type="given">Marianna</namePart>
            <namePart type="family">Apidianaki</namePart>
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        <name type="personal">
            <namePart type="given">Saif</namePart>
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            <namePart type="family">Mohammad</namePart>
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            <namePart type="given">Daniel</namePart>
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            <namePart type="given">David</namePart>
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    <abstract>This paper describes the system developed at LIA for the SemEval-2017 evaluation campaign. The goal of Task 4.A was to identify sentiment polarity in tweets. The system is an ensemble of Deep Neural Network (DNN) models: Convolutional Neural Network (CNN) and Recurrent Neural Network Long Short-Term Memory (RNN-LSTM). We initialize the input representation of DNN with different sets of embeddings trained on large datasets. The ensemble of DNNs are combined using a score-level fusion approach. The system ranked 2nd at SemEval-2017 and obtained an average recall of 67.6%.</abstract>
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    <identifier type="doi">10.18653/v1/S17-2128</identifier>
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%0 Conference Proceedings
%T LIA at SemEval-2017 Task 4: An Ensemble of Neural Networks for Sentiment Classification
%A Rouvier, Mickael
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F rouvier-2017-lia
%X This paper describes the system developed at LIA for the SemEval-2017 evaluation campaign. The goal of Task 4.A was to identify sentiment polarity in tweets. The system is an ensemble of Deep Neural Network (DNN) models: Convolutional Neural Network (CNN) and Recurrent Neural Network Long Short-Term Memory (RNN-LSTM). We initialize the input representation of DNN with different sets of embeddings trained on large datasets. The ensemble of DNNs are combined using a score-level fusion approach. The system ranked 2nd at SemEval-2017 and obtained an average recall of 67.6%.
%R 10.18653/v1/S17-2128
%U https://aclanthology.org/S17-2128/
%U https://doi.org/10.18653/v1/S17-2128
%P 760-765

Markdown (Informal)

[LIA at SemEval-2017 Task 4: An Ensemble of Neural Networks for Sentiment Classification](https://aclanthology.org/S17-2128/) (Rouvier, SemEval 2017)

• LIA at SemEval-2017 Task 4: An Ensemble of Neural Networks for Sentiment Classification (Rouvier, SemEval 2017)

ACL

• Mickael Rouvier. 2017. LIA at SemEval-2017 Task 4: An Ensemble of Neural Networks for Sentiment Classification. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 760–765, Vancouver, Canada. Association for Computational Linguistics.