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 is based on the length of matching subsequences. It is used in the Python library difflib.

Syntax

evaluate

The function evaluate returns the litteral distance between two strings (a value of 0 being identical). While some distances are bounded by 1, other distances like Hamming, Levenshtein, Damerau-Levenshtein, Jaccard can be higher than 1.

using StringDistances
evaluate(Hamming(), "martha", "marhta")
#> 2
evaluate(QGram(2), "martha", "marhta")
#> 6

compare

The higher level function compare directly computes a similarity score between 0 and 1, based on the inverse distance between two strings. 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

Modifiers

The package defines a number of ways to modify string metrics:

• Winkler boosts the similary score of strings with common prefixes

  compare(Jaro(), "martha", "marhta")
  #> 0.9444444444444445
  compare(Winkler(Jaro()), "martha", "marhta")
  #> 0.9611111111111111
  

  The Winkler adjustment was originally defined for the Jaro distance but this package defines it for any string distance.

  compare(QGram(2), "william", "williams")
  #> 0.9230769230769231
  compare(Winkler(QGram(2)), "william", "williams")
  #> 0.9538461538461539
  

• The Python library fuzzywuzzy defines a few modifiers for the RatcliffObershelp distance. This package defines them for any string distance:

  • Partial adjusts for differences in string lengths. The function returns the maximal similarity score between the shorter string and all 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.

    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
    

• You can compose multiple modifiers:

  compare(Winkler(Partial(Jaro())),"mariners vs angels", "los angeles angels at seattle mariners")
  #> 0.7378917378917379
  compare(TokenSet(Partial(RatcliffObershel())),"mariners vs angels", "los angeles angels at seattle mariners")
  #> 1.0
  

Tips

• Each distance is tailored to a specific problem. Edit distances works well with local spelling errors, the Ratcliff-Obsershelp distance works well with edited texts, the Jaro Winkler distance was invented for short strings such as person names, the QGrams distances works well with strings composed of multiple words with fluctuating orderings.

• Most distances perform poorly when comparing company or individual names, where each string is composed of multiple words.

  • While word ordering is mostly irrelevant in this situation, edit distances heavily penalize different orderings. Instead, use either a distance robust to word order (like QGram distances), or compose a distance with TokenSort, which reorders the words alphabetically.

    compare(RatcliffObershelp(), "mariners vs angels", "angels vs mariners")
    #> 0.44444
    compare(TokenSort(RatcliffObershelp()),"mariners vs angels", "angels vs mariners")
    #> 1.0
    compare(Cosine(3), "mariners vs angels", "angels vs mariners")
    #> 0.8125
    

  • General words (like "bank", "company") may appear in one string but no the other. One solution is to abbreviate these common names first to diminish their importance (ie "bk" "co"). Another solution is to use something like the Partial or TokenSet modifiers.

• Standardize strings before comparing them (lowercase, punctuation, whitespaces, accents, abbreviations...)

References

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