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From "ASF GitHub Bot (JIRA)" <>
Subject [jira] [Work logged] (TEXT-155) Add a generic SetSimilarity measure
Date Thu, 07 Mar 2019 13:48:00 GMT


ASF GitHub Bot logged work on TEXT-155:

                Author: ASF GitHub Bot
            Created on: 07/Mar/19 13:47
            Start Date: 07/Mar/19 13:47
    Worklog Time Spent: 10m 
      Work Description: aherbert commented on pull request #109: TEXT-155: Add a generic IntersectionSimilarity
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Issue Time Tracking

            Worklog Id:     (was: 209579)
            Time Spent: 10m
    Remaining Estimate: 0h

> Add a generic SetSimilarity measure
> -----------------------------------
>                 Key: TEXT-155
>                 URL:
>             Project: Commons Text
>          Issue Type: New Feature
>    Affects Versions: 1.6
>            Reporter: Alex D Herbert
>            Priority: Minor
>          Time Spent: 10m
>  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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