--- title: "Edit distance" summary: "Edit distance counts the minimum number of single-character insertions, deletions or substitutions needed to turn one string into another; Levenshtein distance is the standard form." canonical_url: https://entitybind.com/glossary/edit-distance last_updated: 2026-10-15 copy: full --- # Edit distance > Edit distance counts the minimum number of single-character insertions, deletions or substitutions needed to turn one string into another; Levenshtein distance is the standard form. Canonical page: https://entitybind.com/glossary/edit-distance Last updated: 2026-10-15 Glossary · Matching methods ## How to read this The definition as the field writes it comes first, weaknesses and all. What Entity Bind does about it comes last, in its own paragraph, so you can take one without the other. [Filed under matching methods](https://entitybind.com/glossary#matching). By [James Rooney](https://entitybind.com/about), founder of Entity Bind · Last revised 15 October 2026 ## What edit distance means 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. ## Where this shows up in Entity Bind Three pages where this term stops being vocabulary and starts changing an answer, a payload or a price. - [Compared with fuzzy matching](https://entitybind.com/compare/fuzzy-matching): A score for how alike two names look, against a decision about which company they name — and the rows where the fuzzy matching you already run wins. - [Blocking and candidate generation](https://entitybind.com/guides/blocking-and-candidate-generation): Comparing every record against every other record is quadratic and therefore impossible at any real scale. That stage is blocking, also called indexing or candidate… - [How it works](https://entitybind.com/how-it-works): The seven-stage cascade in order, what each stage can settle on its own, and what it hands to the next. The fastest way past a definition is a file: which of your rows are the same company, which of your existing matches we would not have made, and what we could not decide. [Start free →](https://app.entitybind.com/app?signup=1) ## The terms next to this one Each with its own sentence, because the next question is usually about the word beside this one. - [Jaro–Winkler](https://entitybind.com/glossary/jaro-winkler): Jaro–Winkler is a string similarity measure that scores character agreement within a sliding window and then boosts pairs sharing a common prefix, which makes it well suited to names and poorly suited to truncation. - [Fuzzy matching](https://entitybind.com/glossary/fuzzy-matching): Fuzzy matching compares strings by similarity rather than equality, returning a score between zero and one — a useful retrieval signal and, on its own, not a calibrated basis for a merge. - [Normalisation](https://entitybind.com/glossary/normalisation): Normalisation rewrites a record into a comparable form — case, punctuation, legal forms, identifier shapes — before any comparison is attempted, and it is where most of the achievable accuracy in entity resolution is won or lost. - [Blocking](https://entitybind.com/glossary/blocking): Blocking is the technique that makes entity resolution affordable by comparing each record only against a small set of plausible candidates instead of against every other record. ## The other 12 terms on this shelf How two records are compared and how the comparison is made cheap enough to run. Blocking, deterministic rules, probabilistic scoring, string distance. - [Normalisation](https://entitybind.com/glossary/normalisation) - [Transliteration](https://entitybind.com/glossary/transliteration) - [Blocking](https://entitybind.com/glossary/blocking) - [Candidate generation](https://entitybind.com/glossary/candidate-generation) - [Deterministic matching](https://entitybind.com/glossary/deterministic-matching) - [Probabilistic matching](https://entitybind.com/glossary/probabilistic-matching) - [Fellegi–Sunter](https://entitybind.com/glossary/fellegi-sunter) - [m-probability](https://entitybind.com/glossary/m-probability) - [u-probability](https://entitybind.com/glossary/u-probability) - [Match weight](https://entitybind.com/glossary/match-weight) - [Fuzzy matching](https://entitybind.com/glossary/fuzzy-matching) - [Jaro–Winkler](https://entitybind.com/glossary/jaro-winkler) Previous, alphabetically: [Deterministic matching](https://entitybind.com/glossary/deterministic-matching) · Next, alphabetically: [Entity](https://entitybind.com/glossary/entity) ## Evidence basis Internal fixtures only. The historical 2026-08-29 per-tier recording covers 317 GB companies with identifier-rich arrivals seeding the catalogue first; the context tier includes country and registered-town prose. Its legacy replay has no full-prompt-verified calls and cannot certify current calibration. The September 5 CI reports cover 1,176 labelled pairs and 705 records over 250 companies using offline stubs. These are not independent customer holdouts or live production validation; the 99% minimum-input product target remains unmet. Scope is company and organisation identity only — not people, vessels, bank-account ownership, sanctions or emissions. Results may not be used for eligibility decisions about individuals, including employment, credit and tenancy. ## Start with your own company records. Create your workspace, verify your email and run your first company file. The Free plan needs no card. Create API keys in your workspace when you need an integration. [Start free](https://app.entitybind.com/app?signup=1) · [All 64 terms](https://entitybind.com/glossary) 300 credits. No card. For a guided review of your file, explore [the Merge Safety Sprint](https://entitybind.com/audit-pack). --- Entity Bind resolves companies and organisations only. It does not resolve people, vessels, bank-account ownership, sanctions exposure, emissions or geolocation, and its results may not be used for eligibility decisions about individuals, including employment, credit and tenancy. This is the Markdown copy of https://entitybind.com/glossary/edit-distance, assembled from the same data the page renders and carrying every sentence, table row, figure and question the page's main content carries. The HTML page is canonical; it adds the layout, the navigation and the interactive parts (the ask panel, the forms), which a copy can name but not run. Every page on the site, with a line on each: https://entitybind.com/llms.txt.