You could use one of the two options for reporting effect size that are
mentioned in the
Wikipedia article on the Mann Whitney Test

https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test

I recently found this test (as implemented in Python's scipy library, under
the name ranksum) useful for sorting keywords from a pair of corpora.

I got the idea from the much-missed Adam Kilgarriff's article on comparing
corpora (https://www.kilgarriff.co.uk/Publications/2001-K-CompCorpIJCL.pdf)

On 14 October 2016 at 02:52, Eric Atwell <e.s.atw...@leeds.ac.uk> wrote:

> Is there a standard metric of overlap between two ranked lists?
> e.g. to measure/score the similarity between top 10 keywords extracted
> using 2 different formulae, such as LL v MI?
> OR e.g. to measure/score the similarity between top 10 hits from Google v
> top 10 hits from Bing for a give search phrase?
> OR e.g. to measure/score the similarity between ranked lists of PoS-tags
> predicted for a word by two rival PoS-taggers in an ensemble tagger?
>
> If these were unranked sets of keywords, i could simply count the
> intersection. But I want to take rank into account in some senible way.
>
> thanks for expert pointers to proven metrics ...
>
> Eric Atwell, Asst Prof, Language@Leeds and Artificial Intelligence groups,
> School of Computing, University of Leeds, Times University of the Year 2017
>
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-- 
Chris Brew, Computational Scientist, Digital Operatives LLC
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