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        <title>Lijunyi at SemEval-2019 Task 9: An attention-based LSTM and ensemble of different models for suggestion mining from online reviews and forums</title>
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    <name type="personal">
        <namePart type="given">Junyi</namePart>
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            <title>Proceedings of the 13th International Workshop on Semantic Evaluation</title>
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            <namePart type="given">Jonathan</namePart>
            <namePart type="family">May</namePart>
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        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Shutova</namePart>
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        <name type="personal">
            <namePart type="given">Aurelie</namePart>
            <namePart type="family">Herbelot</namePart>
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        <name type="personal">
            <namePart type="given">Xiaodan</namePart>
            <namePart type="family">Zhu</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <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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    <abstract>In this paper, we describe a suggestion mining system that participated in SemEval 2019 Task 9, SubTask A - Suggestion Mining from Online Reviews and Forums. Given some suggestions from online reviews and forums that can be classified into suggestion and non-suggestion classes. In this task, we combine the attention mechanism with the LSTM model, which is the final system we submitted. The final submission achieves 14th place in Task 9, SubTask A with the accuracy of 0.6776. After the challenge, we train a series of neural network models such as convolutional neural network(CNN), TextCNN, long short-term memory(LSTM) and C-LSTM. Finally, we make an ensemble on the predictions of these models and get a better result.</abstract>
    <identifier type="citekey">li-2019-lijunyi</identifier>
    <identifier type="doi">10.18653/v1/S19-2212</identifier>
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        <date>2019-06</date>
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%0 Conference Proceedings
%T Lijunyi at SemEval-2019 Task 9: An attention-based LSTM and ensemble of different models for suggestion mining from online reviews and forums
%A Li, Junyi
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F li-2019-lijunyi
%X In this paper, we describe a suggestion mining system that participated in SemEval 2019 Task 9, SubTask A - Suggestion Mining from Online Reviews and Forums. Given some suggestions from online reviews and forums that can be classified into suggestion and non-suggestion classes. In this task, we combine the attention mechanism with the LSTM model, which is the final system we submitted. The final submission achieves 14th place in Task 9, SubTask A with the accuracy of 0.6776. After the challenge, we train a series of neural network models such as convolutional neural network(CNN), TextCNN, long short-term memory(LSTM) and C-LSTM. Finally, we make an ensemble on the predictions of these models and get a better result.
%R 10.18653/v1/S19-2212
%U https://aclanthology.org/S19-2212
%U https://doi.org/10.18653/v1/S19-2212
%P 1208-1212

Markdown (Informal)

[Lijunyi at SemEval-2019 Task 9: An attention-based LSTM and ensemble of different models for suggestion mining from online reviews and forums](https://aclanthology.org/S19-2212) (Li, SemEval 2019)

• Lijunyi at SemEval-2019 Task 9: An attention-based LSTM and ensemble of different models for suggestion mining from online reviews and forums (Li, SemEval 2019)

ACL

• Junyi Li. 2019. Lijunyi at SemEval-2019 Task 9: An attention-based LSTM and ensemble of different models for suggestion mining from online reviews and forums. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1208–1212, Minneapolis, Minnesota, USA. Association for Computational Linguistics.