Topic-based agreement and disagreement in US electoral manifestos

Menini, Stefano ; Nanni, Federico ; Ponzetto, Simone Paolo ; Tonelli, Sara

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URN: urn:nbn:de:bsz:180-madoc-424901
Document Type: Conference or workshop publication
Year of publication: 2017
Book title: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Page range: 2938-2944
Conference title: EMNLP 2017: Conference on Empirical Methods in Natural Language Processing
Location of the conference venue: Kopenhagen
Date of the conference: 07.-11.09.2017
Place of publication: Stroudsburg, PA
Publishing house: Association for Computational Linguistics
ISBN: 978-1-945626-83-8
Publication language: English
Institution: School of Business Informatics and Mathematics > Information Systems III: Enterprise Data Analysis (Ponzetto 2016-)
Subject: 004 Computer science, internet
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.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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