  Debate portals and similar web platforms constitute one of the main text
sources in computational argumentation research and its applications. While the
corpora built upon these sources are rich of argumentatively relevant content
and structure, they also include text that is irrelevant, or even detrimental,
to their purpose. In this paper, we present a precision-oriented approach to
detecting such irrelevant text in a semi-supervised way. Given a few seed
examples, the approach automatically learns basic lexical patterns of relevance
and irrelevance and then incrementally bootstraps new patterns from sentences
matching the patterns. In the existing args.me corpus with 400k argumentative
texts, our approach detects almost 87k irrelevant sentences, at a precision of
0.97 according to manual evaluation. With low effort, the approach can be
adapted to other web argument corpora, providing a generic way to improve
corpus quality.
