I am new to text processing and stuck on a problem to identify the similarity of columns. To detail the problem, consider we have two columns with string values:

Column A      |        Column B
abcd          |          xyz
foo           |          bar
xyzzy         |          acct
xyz           |          world
onex          |          foo
...           |          ...
...           |          ...

The length of columns can be in order of thousands. Is there an approach to identify how similar the columns are?

Currently, I am creating Minhash signatures for both the columns and computing the Jaccard similarity b/w the signatures. But the problem is, the similarity scores are coming too low even for the columns which have a considerate overlap of values.

Then, I tried creating signatures by taking fractions of values that are most frequently occurring but that does not seem to help either.

Is there any other approach to work on this?

  • $\begingroup$ Maybe revise your code... MinHash seems completely redundant for an order of thousands of strings. If you can see a "considerate overlap" Jaccard similarity should show it too. $\endgroup$
    – Valentas
    Nov 12, 2021 at 9:27
  • $\begingroup$ Do you want to take into account the string similarity between pairs of strings, or just the number of strings in common? $\endgroup$
    – Erwan
    Nov 12, 2021 at 11:45

1 Answer 1


You could use similarity metrics for strings. There are a number of "off the shelf" packages to compare string similarity, such as stringdist for R.

The stringsim function - for instance - allows you to compare string similarity (and there are options to use different metrics).

Example (in R):


stringsim("cat", "catfish")
> [1] 0.4285714

# Also works with vectors
df = data.frame(a=c("cat","dog","tree"),b=c("catfish","hotdog","forest"))

stringsim(df$a,df$b, method="jaccard")
> [1] 0.4285714 0.6000000 0.5000000

Also see this github-repo for fuzzy-matching etc.


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