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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Work logged] (TEXT-155) Add a generic SetSimilarity measure
Date Sat, 09 Mar 2019 04:17:00 GMT

     [ https://issues.apache.org/jira/browse/TEXT-155?focusedWorklogId=210455&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-210455
]

ASF GitHub Bot logged work on TEXT-155:
---------------------------------------

                Author: ASF GitHub Bot
            Created on: 09/Mar/19 04:16
            Start Date: 09/Mar/19 04:16
    Worklog Time Spent: 10m 
      Work Description: kinow commented on issue #109: TEXT-155: Add a generic IntersectionSimilarity
measure
URL: https://github.com/apache/commons-text/pull/109#issuecomment-471143821
 
 
   >The new API using Collection is done. The class can now support duplicates.
   >
   >I have added a test to show the class can produce the same result as a case insensitive
word bigram algorithm documented here: How to Strike a Match.
   
   Does it mean that the code in this pull request can be used to calculate the jaccard index,
F1 score/sorensen-dice, as well as sorensen-dice with bigrams? If so we can think later what
to do with #103 
   
   >Note: Somewhere between switching computers the git history broke and causes a conflict
when trying to rebase. It is only 4 files so when finished (merge or not) I'll drop the branch
and redo with the final files.
   
   All good :+1: 
   
   Added a few comments. Thanks for the link to the _How to Strike a Match_ article. Very
interesting! For that problem, at the moment, I would know only the solution using a more
complete NLP library and something like [word embedding](https://en.wikipedia.org/wiki/Word_embedding)
combined with some machine learning algorithm to train (which requires a lot of data, and
still gives weird results). Having an edit distance that does something similar sounds quite
useful for prototyping or even as a simpler solution.
   
   Recently - digressing - I needed to remove contractions in Python, and the best out-of-the-box
solution I found was [pycontractions](https://github.com/ian-beaver/pycontractions) which
does not scale well for thousands/million requests (maybe not even hundreds) and takes a long
time to initialize some models. Plus there are some catches with one of its approaches for
matching regexes for things like _U.S._. So I had to build a simpler solution for my own case,
but that won't work in other projects.
 
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Issue Time Tracking
-------------------

    Worklog Id:     (was: 210455)
    Time Spent: 3h  (was: 2h 50m)

> Add a generic SetSimilarity 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
>            Priority: Minor
>          Time Spent: 3h
>  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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