how is PcGw computed in quanteda's Naive Bayes?

Question

Consider the usual example that replicates example from 13.1 of An Introduction to Information Retrieval https://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf

txt <- c(d1 = "Chinese Beijing Chinese",
         d2 = "Chinese Chinese Shanghai",
         d3 = "Chinese Macao",
         d4 = "Tokyo Japan Chinese",
         d5 = "Chinese Chinese Chinese Tokyo Japan")

trainingset <- dfm(txt, tolower = FALSE)
trainingclass <- factor(c("Y", "Y", "Y", "N", NA), ordered = TRUE)

tmod1 <- textmodel_nb(trainingset, y = trainingclass, prior = "docfreq")

According to the docs, PcGw is the posterior class probability given the word. How it is computed? I thought what we cared about was the other way around, that is P(word / class).

> tmod1$PcGw
       features
classes   Chinese   Beijing  Shanghai     Macao     Tokyo     Japan
      N 0.1473684 0.2058824 0.2058824 0.2058824 0.5090909 0.5090909
      Y 0.8526316 0.7941176 0.7941176 0.7941176 0.4909091 0.4909091

Thanks!