Bootstrapping an Unsupervised Approach for Classifying Agreement and Disagreement

Opitz, Bernd ; Zirn, Cäcilia

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Document Type: Conference or workshop publication
Year of publication: 2013
Book title: Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013); May 22-24; 2013; Oslo University; Norway
The title of a journal, publication series: Linköping Electronic Conference Proceedings
Volume: 85
Page range: 253-263
Location of the conference venue: Oslo, Norway
Date of the conference: May 22–24, 2013
Publisher: Oepen, Stephan
Place of publication: Linköping
Publishing house: Linköping Univ. Electronic Press
ISBN: 978-91-7519-589-6
ISSN: 1650-3686 , 1650-3740
Publication language: English
Institution: School of Business Informatics and Mathematics > Praktische Informatik II (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Abstract: People tend to have various opinions about topics. In discussions, they can either agree or disagree with another person. The recognition of agreement and disagreement is a useful prerequisite for many applications. It could be used by political scientists to measure how controversial political issues are, or help a company to analyze how well people like their new products. In this work, we develop an approach for recognizing agreement and disagreement. However, this is a challenging task. While keyword-based approaches are only able to cover a limited set of phrases, machine learning approaches require a large amount of training data. We therefore combine advantages of both methods by using a bootstrapping approach. With our completely unsupervised technique, we achieve an accuracy of 72.85%. Besides, we investigate the limitations of a keyword based approach and a machine learning approach in addition to comparing various sets of features.
Additional information: Online ressource. - NEALT proceedings series ; 16. -

Dieser Eintrag ist Teil der Universitätsbibliographie.

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