Building Better Machine Learning Models for Rhetorical Analyses: The Use of Rhetorical Feature Sets for Training Artificial Neural Network Models
Zoltan P. Majdik, James Wynn
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.
Topics & Concepts
Rhetorical questionComputer scienceArtificial intelligenceArtificial neural networkFeature (linguistics)Feature engineeringWord (group theory)Natural language processingMachine learningLanguage modelDeep learningLinguisticsPhilosophyTopic ModelingNatural Language Processing TechniquesSentiment Analysis and Opinion Mining