Bootstrapping an Unsupervised Approach for Classifying Agreement and Disagreement

URL:	http://www.ep.liu.se/ecp/085/023/ecp1385023.pdf
Weitere URL:	http://emmtee.net/oe/nodalida13/conference/28.pdf
Dokumenttyp:	Konferenzveröffentlichung
Erscheinungsjahr:	2013
Buchtitel:	Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013); May 22-24; 2013; Oslo University; Norway
Titel einer Zeitschrift oder einer Reihe:	Linköping Electronic Conference Proceedings
Band/Volume:	85
Seitenbereich:	253-263
Veranstaltungsort:	Oslo, Norway
Veranstaltungsdatum:	May 22–24, 2013
Herausgeber:	Oepen, Stephan
Ort der Veröffentlichung:	Linköping
Verlag:	Linköping Univ. Electronic Press
ISBN:	978-91-7519-589-6
ISSN:	1650-3686 , 1650-3740
Sprache der Veröffentlichung:	Englisch
Einrichtung:	Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Fachgebiet:	004 Informatik
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.
Zusätzliche Informationen:	Online ressource. - NEALT proceedings series ; 16. - http://emmtee.net/oe/nodalida13/conference/28.pdf