Given a language L, a target word w, a [Zipfian] distribution of word frequencies (Rank(w)) and a [gamma] distribution of sentence lengths, can we come up with a way to determine the approximate count for how many sentences have w as the max(Rank)? That is, how many sentences have w as the least-frequent word?
Another way of phrasing it might be: given a word w, what is the probability that a sentence containing w also contains some word w', such that rank(w') > rank(w)?
Naturally this can be determined empirically by looking at a large corpus of a billion or so sentences and calculating the answer, but I'm curious if there's a way around that. Perhaps a sample of sentences gives us enough insight?
In the worst case I have to compute the answer and then use the results to infer similarities in other languages. If so, what's a recommended corpus of sentences & frequency list?