We present the GrASP algorithm for automatically extracting patterns that characterize subtle linguistic phenomena. To that end, GrASP augments each term of input text with multiple layers of linguistic information. These different facets of the text terms are systematically combined to reveal rich patterns. We report highly promising experimental results in several challenging text analysis tasks within the field of Argumentation Mining. We believe that GrASP is general enough to be useful for other domains too.