Abstract

This paper presents our submissions to SemEval-2019 Task9, Suggestion Mining. Our system is one in a series of systems in which we compare an approach using expert-defined rules with a comparable one using machine learning. We target tasks with a syntactic or semantic component that might be better described by a human understanding the task than by a machine learner only able to count features. For Semeval-2019 Task 9, the expert rules clearly outperformed our machine learning model when training and testing on equally balanced testsets.