Language identifier profile comparison favors large profiles
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Key: TIKA-496
URL: https://issues.apache.org/jira/browse/TIKA-496
Project: Tika
Issue Type: Bug
Components: languageidentifier
Affects Versions: 0.7
Reporter: Jan Høydahl
I think I've found a flaw in the distance algorithm.
In LanguageProfile.java distance() method, we normalize the frequency for an
ngram by dividing by the total count.
The total count for a profile is simply the sum of all counts in the profile.
Problem is, that the .ngp files are cutoff at 1000 entries, and the total count
is then the sum of all those 1000 entries.
However, there will be a long-tail of lower frequency ngrams which are cut off
and therefore not included in the total count.
Effect is that the ngrams from profiles with large training set are more
important than ngrams from smaller training set.
You can see this effect especially well when classifying short texts in a
language wich has similar sister languages with larger training sets. My
example is "no" vs "da".
Sample from the tail of "no.ngp":
_gå 461
ask 461
ria 459
små 459
...and from the tail of "dk.ngp":
dbr 966
ost 966
ævn 964
It is obvious that "dk" has a longer tail after cutoff than "no" and therefore
a larger sum.
A solution is to count the real total count when generating the .ngp file and
storing the total in the profile file itself, instead of counting when loading
the cutoff profile.
Alterniatvely, normalize counts before writing the .ngp file, so that the top
entry is always 100000
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