This Julia package computes various distances between strings.

Distances

Edit Distances

• Hamming Distance
• Levenshtein Distance
• Damerau-Levenshtein Distance

Q-Grams Distances

Q-gram distances compare the set of all substrings of length q in each string.

• QGram Distance
• Cosine Distance
• Jaccard Distance
• Overlap Distance
• Sorensen-Dice Distance

Others

• Jaro Distance
• RatcliffObershelp Distance

Syntax

The function compare returns a similarity score between two strings, based on their distance. The similarity score is always between 0 and 1. A value of 0 being completely different and a value of 1 being completely similar.

using StringDistances
compare(Hamming(), "martha", "marhta")
#> 0.6666666666666667
compare(QGram(2), "martha", "marhta")
#> 0.4

To return the litteral distance between two strings, use evaluate

Modifiers

The package includes distance "modifiers", that can be applied to any distance. Read below for more details.

• Winkler boosts the similary score of strings with common prefixes. The Winkler adjustment was originally defined for the Jaro similarity score but this package defines it for any string distance.

  compare(Jaro(), "martha", "marhta")
  #> 0.9444444444444445
  compare(Winkler(Jaro()), "martha", "marhta")
  #> 0.9611111111111111
  
  compare(QGram(2), "william", "williams")
  #> 0.9230769230769231
  compare(Winkler(QGram(2)), "william", "williams")
  #> 0.9538461538461539
  

• Modifiers from the Python library fuzzywuzzy .

  • Partial returns the maximal similarity score between the shorter string and substrings of the longer string.

    compare(Levenshtein(), "New York Yankees", "Yankees")
    #> 0.4375
    compare(Partial(Levenshtein()), "New York Yankees", "Yankees")
    #> 1.0
    

  • TokenSort adjusts for differences in word orders by reording words alphabetically.

    compare(RatcliffObershelp(), "mariners vs angels", "angels vs mariners")
    #> 0.44444
    compare(TokenSort(RatcliffObershelp()),"mariners vs angels", "angels vs mariners")
    #> 1.0
    

  • TokenSet adjusts for differences in word orders and word numbers by comparing the intersection of two strings with each string.

    compare(Jaro(),"mariners vs angels", "los angeles angels at seattle mariners")
    #> 0.559904
    compare(TokenSet(Jaro()),"mariners vs angels", "los angeles angels at seattle mariners")
    #> 0.944444
    

  • TokenMax combines scores using the base distance, the Partial, TokenSort and TokenSet modifiers, with penalty terms depending on string lengths.

    compare(TokenMax(RatcliffObershelp()),"mariners vs angels", "los angeles angels at seattle mariners")
    #> 0.855
    

Which distance should I use?

It depends on your specific problem. As a rule of thumb,

• standardize strings before comparing them (lowercase, punctuation, whitespaces, accents, abbreviations...)
• if word order does not matter, avoid edit distances.

References

• The stringdist Package for Approximate String Matching Mark P.J. van der Loo
• fuzzywuzzy blog post