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https://issues.apache.org/jira/browse/TEXT-126?focusedWorklogId=210489&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-210489
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ASF GitHub Bot logged work on TEXT-126:
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Author: ASF GitHub Bot
Created on: 09/Mar/19 06:48
Start Date: 09/Mar/19 06:48
Worklog Time Spent: 10m
Work Description: ameyjadiye commented on issue #103: TEXT-126: Adding
Sorensen-Dice similarity algoritham
URL: https://github.com/apache/commons-text/pull/103#issuecomment-471152055
@kinow @aherbert
I have written code by keeping Wikipedia as standard and researched some
other libraries from other languages just for reference. all of them are using
bigrams for calculating similarities. and I personally think that if we go
further and use triGram, qurterGram ... nGram the resulting %age would be
incorrect. we can't use charachter by charachter match i.e uniGram as that will
also make result bad as ```ab!=ba```
with nGram are we tampering the existing proved algoritham ? if its giving
better results than existing algo I'm ok with that, also does someone really
need it in real world examples ?
Wikipedia says
> When taken as a string similarity measure, the coefficient may be
calculated for two strings, x and y using bigrams as follows:[9]
>
> s=2nt / nx + ny
> where nt is the number of character bigrams found in both strings, nx is
the number of bigrams in string x and ny is the number of bigrams in string y.
For example, to calculate the similarity between:
>
> night
> nacht
> We would find the set of bigrams in each word:
>
> {ni,ig,gh,ht}
> {na,ac,ch,ht}
> Each set has four elements, and the intersection of these two sets has
only one element: ht.
>
> Inserting these numbers into the formula, we calculate, s = (2 · 1) / (4 +
4) = 0.25.
>
not sure but are we over engineering similarities with #109 ? let me know if
there is practicle use of nGram in real world ? would like to study it more.
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Issue Time Tracking
-------------------
Worklog Id: (was: 210489)
Time Spent: 10h 20m (was: 10h 10m)
> Dice's Coefficient Algorithm in String similarity
> -------------------------------------------------
>
> Key: TEXT-126
> URL: https://issues.apache.org/jira/browse/TEXT-126
> Project: Commons Text
> Issue Type: Improvement
> Reporter: Vicky Chawda
> Priority: Major
> Time Spent: 10h 20m
> 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* - [http://www.catalysoft.com/articles/StrikeAMatch.html]
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