Edit distance

Edit distance is a count of keystrokes: how many single-character insertions, deletions or substitutions turn one string into the other. Levenshtein distance is the canonical form — 'Acme' to 'Acmee' is one insertion, distance 1. It counts keystrokes, not meaning. Damerau–Levenshtein adds transposition as a single operation, which matters because transposed characters are one of the most common typing errors. Normalising the distance by the longer string's length converts it to a similarity in [0,1] that can be compared across pairs of different lengths.

Edit distance models typing and OCR errors well and models everything else badly. It has no concept of tokens, so reordering two words is expensive; no concept of meaning, so 'Limited' and 'Ltd' are distant; and no concept of importance, so a one-character difference in a rare token counts the same as one in 'Services'. Company names break all three assumptions routinely.

It is also quadratic in string length, which is why it belongs in the scoring stage on a shortlist rather than in the retrieval stage over a catalogue. Blocking finds the candidates cheaply; edit distance grades them.

In a company pipeline it is best used after normalisation and on tokens rather than on whole strings, and always beside a rarity signal, so that a small distance on a distinctive token counts for more than a small distance on a generic one. Entity Bind treats string similarity this way: as one weighted feature entering a fitted score, never as the score itself.