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        <title>NRU-HSE at SemEval-2017 Task 4: Tweet Quantification Using Deep Learning Architecture</title>
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    <name type="personal">
        <namePart type="given">Nikolay</namePart>
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            <namePart type="given">Steven</namePart>
            <namePart type="family">Bethard</namePart>
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            <namePart type="given">Marine</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>
            <namePart type="family">Jurgens</namePart>
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    <abstract>In many areas, such as social science, politics or market research, people need to deal with dataset shifting over time. Distribution drift phenomenon usually appears in the field of sentiment analysis, when proportions of instances are changing over time. In this case, the task is to correctly estimate proportions of each sentiment expressed in the set of documents (quantification task). Basically, our study was aimed to analyze the effectiveness of a mixture of quantification technique with one of deep learning architecture. All the techniques are evaluated using the SemEval-2017 Task4 dataset and source code, mentioned in this paper and available online in the Python programming language. The results of an application of the quantification techniques are discussed.</abstract>
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%0 Conference Proceedings
%T NRU-HSE at SemEval-2017 Task 4: Tweet Quantification Using Deep Learning Architecture
%A Karpov, Nikolay
%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 karpov-2017-nru
%X In many areas, such as social science, politics or market research, people need to deal with dataset shifting over time. Distribution drift phenomenon usually appears in the field of sentiment analysis, when proportions of instances are changing over time. In this case, the task is to correctly estimate proportions of each sentiment expressed in the set of documents (quantification task). Basically, our study was aimed to analyze the effectiveness of a mixture of quantification technique with one of deep learning architecture. All the techniques are evaluated using the SemEval-2017 Task4 dataset and source code, mentioned in this paper and available online in the Python programming language. The results of an application of the quantification techniques are discussed.
%R 10.18653/v1/S17-2113
%U https://aclanthology.org/S17-2113
%U https://doi.org/10.18653/v1/S17-2113
%P 683-688

Markdown (Informal)

[NRU-HSE at SemEval-2017 Task 4: Tweet Quantification Using Deep Learning Architecture](https://aclanthology.org/S17-2113) (Karpov, SemEval 2017)

• NRU-HSE at SemEval-2017 Task 4: Tweet Quantification Using Deep Learning Architecture (Karpov, SemEval 2017)

ACL

• Nikolay Karpov. 2017. NRU-HSE at SemEval-2017 Task 4: Tweet Quantification Using Deep Learning Architecture. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 683–688, Vancouver, Canada. Association for Computational Linguistics.