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        <title>Topic-Based Agreement and Disagreement in US Electoral Manifestos</title>
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        <namePart type="given">Stefano</namePart>
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        <namePart type="given">Federico</namePart>
        <namePart type="family">Nanni</namePart>
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        <namePart type="given">Simone</namePart>
        <namePart type="given">Paolo</namePart>
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        <namePart type="given">Sara</namePart>
        <namePart type="family">Tonelli</namePart>
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            <title>Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Martha</namePart>
            <namePart type="family">Palmer</namePart>
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            <namePart type="given">Rebecca</namePart>
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    <abstract>We present a topic-based analysis of agreement and disagreement in political manifestos, which relies on a new method for topic detection based on key concept clustering. Our approach outperforms both standard techniques like LDA and a state-of-the-art graph-based method, and provides promising initial results for this new task in computational social science.</abstract>
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%0 Conference Proceedings
%T Topic-Based Agreement and Disagreement in US Electoral Manifestos
%A Menini, Stefano
%A Nanni, Federico
%A Ponzetto, Simone Paolo
%A Tonelli, Sara
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F menini-etal-2017-topic
%X We present a topic-based analysis of agreement and disagreement in political manifestos, which relies on a new method for topic detection based on key concept clustering. Our approach outperforms both standard techniques like LDA and a state-of-the-art graph-based method, and provides promising initial results for this new task in computational social science.
%R 10.18653/v1/D17-1318
%U https://aclanthology.org/D17-1318
%U https://doi.org/10.18653/v1/D17-1318
%P 2938-2944

Markdown (Informal)

[Topic-Based Agreement and Disagreement in US Electoral Manifestos](https://aclanthology.org/D17-1318) (Menini et al., EMNLP 2017)

• Topic-Based Agreement and Disagreement in US Electoral Manifestos (Menini et al., EMNLP 2017)

ACL

• Stefano Menini, Federico Nanni, Simone Paolo Ponzetto, and Sara Tonelli. 2017. Topic-Based Agreement and Disagreement in US Electoral Manifestos. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2938–2944, Copenhagen, Denmark. Association for Computational Linguistics.