Levenshtein distance
The levenshtein distance is used for measuring
the “distance” or similarity of two character strings. Other similarity
algorithms can be supplied to the code that does the matching.
This code is used in pot2po, tmserver and Virtaal. It is implemented in the toolkit, but can optionally
use the fast C implementation provided by python-Levenshtein if it
is installed. It is strongly recommended that python-levenshtein be installed.
To exercise the code the classfile “Levenshtein.py” can be executed directly
with:
python Levenshtein.py "The first string." "The second string"
Note
Remember to quote the two parameters.
The following things should be noted:
- Only the first MAX_LEN characters are considered. Long strings differing at
the end will therefore seem to match better than they should. A penalty is
awarded if strings are shortened.
- The calculation can stop prematurely as soon as it realise that the supplied
minimum required similarity can not be reached. Strings with widely different
lengths give the opportunity for this shortcut. This is by definition of the
Levenshtein distance: the distance will be at least as much as the difference
in string length. Similarities lower than your supplied minimum (or the
default) should therefore not be considered authoritive.
Shortcommings
The following shortcommings have been identified:
- Cases sensitivity: ‘E’ and ‘e’ are considered different characters and
according differ as much as ‘z’ and ‘e’. This is not ideal, as case
differences should be considered less of a difference.
- Diacritics: ‘ê’ and ‘e’ are considered different characters and according
differ as much as ‘z’ and ‘e’. This is not ideal, as missing diacritics could
be due to small input errors, or even input data that simply do not have the
correct diacritics.
- Words that have similar characters, but are different, could increase the
similarity beyond what is wanted. The sentences “It is though.” and “It is
dough.” differ markedly semantically, but score similarity of almost 85%. A
possible solution is to do an additional calculation based on words, instead
of characters.
- Whitespace: Differences in tabs, newlines, and space usage should perhaps be
considered as a special case.