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Bootstrapping Supervised Machine-learning Polarity Classifiers with Rule-based Classification

  • 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.

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Author:Michael WiegandGND, Dietrich Klakow
Parent Title (English):Proceedings of the 1st Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA), August 17 2010, Lisbon, Portugal
Publisher:Universidad de Alicante
Place of publication:Alicante
Document Type:Conference Proceeding
Year of first Publication:2010
Date of Publication (online):2019/01/24
GND Keyword:Computerlinguistik; Information Extraction; Maschinelles Lernen; Natürliche Sprache; Polarität
First Page:59
Last Page:66
DDC classes:400 Sprache / 400 Sprache, Linguistik
Open Access?:ja
Licence (German):License LogoUrheberrechtlich geschützt