Abstract
This paper presents the results of developing subjectivity classifiers using only unannotated texts for training. The performance rivals that of previous supervised learning approaches. In addition, we advance the state of the art in objective sentence classification by learning extraction patterns associated with objectivity and creating objective classifiers that achieve substantially higher recall than previous work with comparable precision.
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Wiebe, J., Riloff, E. (2005). Creating Subjective and Objective Sentence Classifiers from Unannotated Texts. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2005. Lecture Notes in Computer Science, vol 3406. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30586-6_53
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DOI: https://doi.org/10.1007/978-3-540-30586-6_53
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