In this study, context-free and context-dependent information measures are applied to a new corpus of tweets and blog posts. The aim is to account for the expressive meaning and characterize the variability of available intensifying items. It comes to light that context-free and context-dependent information measures are highly correlated and account for the distribution of intensifiers in the data, giving credence to the notion that intensifiers form a common word class, even across syntactic and semantic differences.

Both information measures show that stacked intensifiers tend to be ordered from least to most expressive within a phrase, i.e., the information tends to increase. We explain this fact using the Uniform Information Density Hypothesis: The first, less expressive intensifier is used to introduce the phrase, ease the reader’s processing load, and smooth the information flow.

BibTex | DOI: 10.5281/zenodo.133837