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From "ASF GitHub Bot (JIRA)" <>
Subject [jira] [Work logged] (TEXT-126) Dice's Coefficient Algorithm in String similarity
Date Wed, 06 Mar 2019 22:59:00 GMT


ASF GitHub Bot logged work on TEXT-126:

                Author: ASF GitHub Bot
            Created on: 06/Mar/19 22:58
            Start Date: 06/Mar/19 22:58
    Worklog Time Spent: 10m 
      Work Description: aherbert commented on issue #103: TEXT-126: Adding Sorensen-Dice similarity
   Hi @ameyjadiye,
   The code is now a good working implementation. However it should be noted that the Sorensen-Dice
similarity is a binary scoring metric. It can be applied to anything where you have two sets
to be matched. It is closely related to the Jaccard score.
   This PR brings in a new algorithm to compute matches using pairs of characters (bigrams).
This is a conflict with existing similarity measures in the package implementing `SimilarityScore<R>`:
   JaccardSimilarity - uses `Set<Character>` for matching single characters to produce
   JaroWinklerSimilarity - uses single character matching to produce an edit distance which
is then converted to a similarity `Double`
   LongestCommonSubsequence - uses single character matching for sub-sequences to produce
   The rest of the package is for an extension of `SimilarityScore<R>` which is `EditDistance<R>`:
   CosineDistance - uses a `RegexTokenizer` on whitespace to match words to produce `Double`
   HammingDistance - uses single character matching to produce `Integer`
   JaccardDistance - just inverts the JaccardSimilarity to produce `Double`
   JaroWinklerDistance - complementary of Jaro-Winkler similarity to produce `Double`
   LevenshteinDetailedDistance - single character changes to produce `LevenshteinResults`
   LevenshteinDistance - single character changes to produce `Integer`
   LongestCommonSubsequenceDistance - single character based substring to produce `Double`
   **So all the other measures use single characters or words** (CosineDistance).
   If you want a Sorensen-Dice distance score using single characters then just use the `JaccardDistance`
and map it: `S = 2J / (1 + J)`
   This class is effectively a JaccardDistance with `bigrams` but with a mapped output score.
   One suggestion would be a change to be something like `BigramJaccardSimilarity` and then
compute the Jaccard using bigrams. Then put new classes in for `SorensenDiceSimilarity` and
   However the current JaccardDistance uses Set<Character> which has space and efficiency
advantages over Set<String>. Once you are using strings then there is no reason to have
only bigrams.
   My preference would be to add a new class `TokenizerJaccardSimilarity` (or something nicer)
that accepts a `Tokenizer<CharSequence>` in the constructor. This then tokenises the
input into two sets and computes the union of the two sets.
   Your `bigram` case can be fulfilled by writing a new `Tokenizer<CharSequence>` that
returns the bigrams. This new class would be flexible for computing the Jaccard for words,
bigrams, trigrams, n-grams, or whatever else you can tokenise a `CharSequence` into.
   Then you add a `TokenSorensonDiceSimilarity` that just maps the Jaccard score. You can
see this for example in the `JaccardDistance` class.
   @kinow Opinions on a more flexible approach?
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Issue Time Tracking

    Worklog Id:     (was: 209220)
    Time Spent: 8.5h  (was: 8h 20m)

> Dice's Coefficient Algorithm in String similarity
> -------------------------------------------------
>                 Key: TEXT-126
>                 URL:
>             Project: Commons Text
>          Issue Type: Improvement
>            Reporter: Vicky Chawda
>            Priority: Major
>          Time Spent: 8.5h
>  Remaining Estimate: 0h
> I'd like to propose an extension to the algorithms for string similarity in *commons-text/src/main/java/org/apache/commons/text/similarity/*
>  Dice's Coefficient Algorithm can be helpful for many who are looking for ranking similarities
in strings.
> *Inspired from* - []

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