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        <title>CNN- and LSTM-based Claim Classification in Online User Comments</title>
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        <namePart type="given">Chinnappa</namePart>
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    <name type="personal">
        <namePart type="given">Iryna</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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        <name type="personal">
            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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    <abstract>When processing arguments in online user interactive discourse, it is often necessary to determine their bases of support. In this paper, we describe a supervised approach, based on deep neural networks, for classifying the claims made in online arguments. We conduct experiments using convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) on two claim data sets compiled from online user comments. Using different types of distributional word embeddings, but without incorporating any rich, expensive set of features, we achieve a significant improvement over the state of the art for one data set (which categorizes arguments as factual vs. emotional), and performance comparable to the state of the art on the other data set (which categorizes propositions according to their verifiability). Our approach has the advantages of using a generalized, simple, and effective methodology that works for claim categorization on different data sets and tasks.</abstract>
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        <url>https://aclanthology.org/C16-1258</url>
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%0 Conference Proceedings
%T CNN- and LSTM-based Claim Classification in Online User Comments
%A Guggilla, Chinnappa
%A Miller, Tristan
%A Gurevych, Iryna
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F guggilla-etal-2016-cnn
%X When processing arguments in online user interactive discourse, it is often necessary to determine their bases of support. In this paper, we describe a supervised approach, based on deep neural networks, for classifying the claims made in online arguments. We conduct experiments using convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) on two claim data sets compiled from online user comments. Using different types of distributional word embeddings, but without incorporating any rich, expensive set of features, we achieve a significant improvement over the state of the art for one data set (which categorizes arguments as factual vs. emotional), and performance comparable to the state of the art on the other data set (which categorizes propositions according to their verifiability). Our approach has the advantages of using a generalized, simple, and effective methodology that works for claim categorization on different data sets and tasks.
%U https://aclanthology.org/C16-1258
%P 2740-2751

Markdown (Informal)

[CNN- and LSTM-based Claim Classification in Online User Comments](https://aclanthology.org/C16-1258) (Guggilla et al., COLING 2016)

• CNN- and LSTM-based Claim Classification in Online User Comments (Guggilla et al., COLING 2016)

ACL

• Chinnappa Guggilla, Tristan Miller, and Iryna Gurevych. 2016. CNN- and LSTM-based Claim Classification in Online User Comments. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2740–2751, Osaka, Japan. The COLING 2016 Organizing Committee.