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From "ASF GitHub Bot (JIRA)" <>
Subject [jira] [Work logged] (TEXT-155) Add a generic OverlapSimilarity measure
Date Sun, 10 Mar 2019 13:37:00 GMT


ASF GitHub Bot logged work on TEXT-155:

                Author: ASF GitHub Bot
            Created on: 10/Mar/19 13:36
            Start Date: 10/Mar/19 13:36
    Worklog Time Spent: 10m 
      Work Description: aherbert commented on issue #109: TEXT-155: Add a generic OverlapSimilarity
   I have tried to clean up the history into a single commit.
   I have changed the name back to `IntersectionSimilarity` as it was pointed out to me that
`Overlap` has a specific meaning in the combinatorics on words space, an “overlap” is
a specific repeated pattern. Also there is an [OverlapCoefficient](
between sets which is the intersection over the min size of the two sets.
   I dropped the computation of the metrics and the union from the `IntersectionResult`. This
class now has no logic but just holds data.
   I removed the use of streams and use a classic iteration over the smaller of the two sets
of keys to get the intersection.
   This is now a generic set similarity which just requires a function to split up a `CharSequence`.
A place to provide such functions, as contained in the example units test, is best left to
another block of new functionality.
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Issue Time Tracking

    Worklog Id:     (was: 210669)
    Time Spent: 4h 50m  (was: 4h 40m)

> Add a generic OverlapSimilarity measure
> ---------------------------------------
>                 Key: TEXT-155
>                 URL:
>             Project: Commons Text
>          Issue Type: New Feature
>    Affects Versions: 1.6
>            Reporter: Alex D Herbert
>            Priority: Minor
>             Fix For: 1.7
>          Time Spent: 4h 50m
>  Remaining Estimate: 0h
> The {{SimilarityScore<T>}} interface can be used to compute a generic result. I
propose to add a class that can compute the intersection between two sets formed from the
characters. The sets must be formed from the {{CharSequence}} input to the {{apply}} method
using a {{Function<CharSequence, Set<T>>}} to convert the {{CharSequence}}. This
function can be passed to the {{SimilarityScore<T>}} during construction.
> The result can then be computed to have the size of each set and the intersection.
> I have created an implementation that can compute the equivalent of the {{JaccardSimilary}}
class by creating {{Set<Character>}} and also the F1-score using bigrams (pairs of characters)
by creating {{Set<String>}}. This relates to [Text-126|]
which suggested an algorithm for the Sorensen-Dice similarity, also known as the F1-score.
> Here is an example:
> {code:java}
> // Match the functionality of the JaccardSimilarity class
> Function<CharSequence, Set<Character>> converter = (cs) -> {
>     final Set<Character> set = new HashSet<>();
>     for (int i = 0; i < cs.length(); i++) {
>         set.add(cs.charAt(i));
>     }
>     return set;
> };
> IntersectionSimilarity<Character> similarity = new IntersectionSimilarity<>(converter);
> IntersectionResult result = similarity.apply("something", "something else");
> {code}
> The result has the size of set A, set B and the intersection between them.
> This class was inspired by my look through the various similarity implementations. All
of them except the {{CosineSimilarity}} perform single character matching between the input
{{CharSequence}}s. The {{CosineSimilarity}} tokenises using whitespace to create words.
> This more generic type of implementation will allow a user to determine how to divide
the {{CharSequence}} but to create the sets that are compared, e.g. single characters, words,
bigrams, etc.

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