  We propose a resampling-based approach for assessing keyness in corpus
linguistics based on suggestions by Gries (2006, 2022). Traditional approaches
based on hypothesis tests (e.g. Likelihood Ratio) model the copora as
independent identically distributed samples of tokens. This model does not
account for the often observed uneven distribution of occurences of a word
across a corpus. When occurences of a word are concentrated in few documents,
large values of LLR and similar scores are in fact much more likely than
accounted for by the token-by-token sampling model, leading to false positives.
  We replace the token-by-token sampling model by a model where corpora are
samples of documents rather than tokens, which is much closer to the way
corpora are actually assembled. We then use a permutation approach to
approximate the distribution of a given keyness score under the null hypothesis
of equal frequencies and obtain p-values for assessing significance. We do not
need any assumption on how the tokens are organized within or across documents,
and the approach works with basically *any* keyness score. Hence, appart from
obtaining more accurate p-values for scores like LLR, we can also assess
significance for e.g. the logratio which has been proposed as a measure of
effect size.
  An efficient implementation of the proposed approach is provided in the `R`
package `keyperm` available from github.
