Building Better Machine Learning Models for Rhetorical Analyses: The Use of Rhetorical Feature Sets for Training Artificial Neural Network Models

Abstract

ABSTRACT 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, which outperforms models with more computing-intensive architectures.