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Building Better Machine Learning Models for Rhetorical Analyses: The Use of Rhetorical Feature Sets for Training Artificial Neural Network Models
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Description: In this paper, we investigate two approaches to building artificial neural network models to compare their effectiveness for accurately classifying rhetorical structures across multiple (non-binary) classes in small textual datasets. We find that the most accurate type of model can be designed by using a custom rhetorical feature list coupled with general-language word vector representations, outperforming models with more computing-intensive architectures.
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