I've had to do this a few times when building simple input normalization features. The trouble with scoring based on Levenshtein distance in that case is that it improperly penalizes phrases that are significantly different in length but similar in content.
For example, let's say I was searching a database of countries for "North Korea". In my list I have:
South Africa (LD: 6)
Congo (LD: 9)
Republic of Korea (LD: 11)
...
Democratic People's Republic of Korea (LD: 28)
The actual answer (DPRK) will be pushed far to the bottom based on a naive ranking that uses LD.
The hack that's worked best for me? Rank based on the number of common two-character substrings between the source and target. It's simple, easy to build an index for, and has surprisingly great results. Its ideal use case is if you don't need to return a single absolute best result and can, say, present the three best to a human being and let them pick the match.
For the above search I'd get the following results using the two-character method:
Republic of Korea: 4
DPRK: 4
South Africa: 1
Congo: 0
For example, let's say I was searching a database of countries for "North Korea". In my list I have:
South Africa (LD: 6) Congo (LD: 9) Republic of Korea (LD: 11) ... Democratic People's Republic of Korea (LD: 28)
The actual answer (DPRK) will be pushed far to the bottom based on a naive ranking that uses LD.
The hack that's worked best for me? Rank based on the number of common two-character substrings between the source and target. It's simple, easy to build an index for, and has surprisingly great results. Its ideal use case is if you don't need to return a single absolute best result and can, say, present the three best to a human being and let them pick the match.
For the above search I'd get the following results using the two-character method:
Republic of Korea: 4 DPRK: 4 South Africa: 1 Congo: 0