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        <title>Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity Classification</title>
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        <namePart type="given">Hussam</namePart>
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            <namePart type="given">Steven</namePart>
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            <namePart type="given">Marine</namePart>
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            <namePart type="given">Marianna</namePart>
            <namePart type="family">Apidianaki</namePart>
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            <namePart type="given">Saif</namePart>
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            <namePart type="family">Mohammad</namePart>
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            <namePart type="given">David</namePart>
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    <abstract>This paper presents Senti17 system which uses ten convolutional neural networks (ConvNet) to assign a sentiment label to a tweet. The network consists of a convolutional layer followed by a fully-connected layer and a Softmax on top. Ten instances of this network are initialized with the same word embeddings as inputs but with different initializations for the network weights. We combine the results of all instances by selecting the sentiment label given by the majority of the ten voters. This system is ranked fourth in SemEval-2017 Task4 over 38 systems with 67.4% average recall.</abstract>
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%0 Conference Proceedings
%T Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity Classification
%A Hamdan, Hussam
%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 hamdan-2017-senti17
%X This paper presents Senti17 system which uses ten convolutional neural networks (ConvNet) to assign a sentiment label to a tweet. The network consists of a convolutional layer followed by a fully-connected layer and a Softmax on top. Ten instances of this network are initialized with the same word embeddings as inputs but with different initializations for the network weights. We combine the results of all instances by selecting the sentiment label given by the majority of the ten voters. This system is ranked fourth in SemEval-2017 Task4 over 38 systems with 67.4% average recall.
%R 10.18653/v1/S17-2116
%U https://aclanthology.org/S17-2116
%U https://doi.org/10.18653/v1/S17-2116
%P 700-703

Markdown (Informal)

[Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity Classification](https://aclanthology.org/S17-2116) (Hamdan, SemEval 2017)

• Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity Classification (Hamdan, SemEval 2017)

ACL

• Hussam Hamdan. 2017. Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity Classification. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 700–703, Vancouver, Canada. Association for Computational Linguistics.