PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Bootstrapping Supervised Machine-learning Polarity Classifiers with Rule-based Classification
Michael Wiegand and Dietrich Klakow
In: ECAI-Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA), 17 Aug 2010, Lisbon, Portugal.

Abstract

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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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:8864
Deposited By:Diana Schreyer
Deposited On:21 February 2012