TY - CHAP U1 - Konferenzveröffentlichung A1 - Wiegand, Michael A1 - Klakow, Dietrich T1 - Bootstrapping Supervised Machine-learning Polarity Classifiers with Rule-based Classification T2 - Proceedings of the 1st Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA), August 17 2010, Lisbon, Portugal N2 - In this paper, we explore the effectiveness of bootstrapping supervised machine-learning polarity classifiers using the output of domain-independent rule-based classifiers. The benefit of this method is that no labeled training data are required. Still, this method allows to capture in-domain knowledge by training the supervised classifier on in-domain features, such as bag of words. We investigate how important the quality of the rule-based classifier is and what features are useful for the supervised classifier. The former addresses the issue in how far relevant constructions for polarity classification, such as word sense disambiguation, negation modeling, or intensification, are important for this self-training approach. We not only compare how this method relates to conventional semi-supervised learning but also examine how it performs under more difficult settings in which classes are not balanced and mixed reviews are included in the dataset. KW - Computerlinguistik KW - Maschinelles Lernen KW - Information Extraction KW - Polarität KW - Natürliche Sprache KW - Sentimentanalyse Y1 - 2010 U6 - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-84473 UN - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-84473 UR - http://gplsi.dlsi.ua.es/congresos/wassa2010/?opc=0 SP - 59 EP - 66 PB - Universidad de Alicante CY - Alicante ER -