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https://issues.apache.org/jira/browse/TEXT-155?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Alex D Herbert resolved TEXT-155.
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Resolution: Implemented
Assignee: Alex D Herbert
> Add a generic OverlapSimilarity measure
> ---------------------------------------
>
> Key: TEXT-155
> URL: https://issues.apache.org/jira/browse/TEXT-155
> Project: Commons Text
> Issue Type: New Feature
> Affects Versions: 1.6
> Reporter: Alex D Herbert
> Assignee: Alex D Herbert
> Priority: Minor
> Fix For: 1.7
>
> Time Spent: 5.5h
> 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|https://issues.apache.org/jira/projects/TEXT/issues/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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