  Natural language processing models learn word representations based on the
distributional hypothesis, which asserts that word context (e.g.,
co-occurrence) correlates with meaning. We propose that $n$-grams composed of
random character sequences, or $garble$, provide a novel context for studying
word meaning both within and beyond extant language. In particular, randomly
generated character $n$-grams lack meaning but contain primitive information
based on the distribution of characters they contain. By studying the
embeddings of a large corpus of garble, extant language, and pseudowords using
CharacterBERT, we identify an axis in the model's high-dimensional embedding
space that separates these classes of $n$-grams. Furthermore, we show that this
axis relates to structure within extant language, including word
part-of-speech, morphology, and concept concreteness. Thus, in contrast to
studies that are mainly limited to extant language, our work reveals that
meaning and primitive information are intrinsically linked.
