A Corpus of 21st Century Scots Texts

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Levenshtein Distance

Enter a word to find nearest neighbouring words, for example ablow

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Similar words to hannet in Corpus

Levenshtein Double Levenshtein SoundEx MetaPhone Manually curated
hannet (0) - 2 freq
hannel (1) - 1 freq
hannit (1) - 20 freq
gannet (1) - 10 freq
bannet (1) - 1 freq
hanker (2) - 3 freq
henner (2) - 3 freq
hinnert (2) - 3 freq
hanzel (2) - 1 freq
mannert (2) - 1 freq
hansel (2) - 16 freq
sonnet (2) - 12 freq
hane (2) - 4 freq
garnet (2) - 1 freq
hunnert (2) - 14 freq
hant (2) - 6 freq
hasnt (2) - 1 freq
hunnel (2) - 4 freq
danner (2) - 13 freq
handen (2) - 1 freq
kennet (2) - 1 freq
hintet (2) - 1 freq
dunnet (2) - 1 freq
manned (2) - 6 freq
hanest (2) - 1 freq
hannet (0) - 2 freq
hannit (1) - 20 freq
haunnit (2) - 2 freq
bannet (2) - 1 freq
hannel (2) - 1 freq
gannet (2) - 10 freq
bunnet (3) - 113 freq
linnet (3) - 1 freq
lannit (3) - 3 freq
haunlet (3) - 2 freq
hanny (3) - 1 freq
rennet (3) - 1 freq
handit (3) - 27 freq
hanns (3) - 23 freq
dinnet (3) - 1 freq
hannle (3) - 5 freq
hantit (3) - 3 freq
bannit (3) - 2 freq
haint (3) - 3 freq
boannet (3) - 1 freq
hankit (3) - 2 freq
hainit (3) - 6 freq
cannit (3) - 1 freq
hairnet (3) - 2 freq
hanna (3) - 11 freq
SoundEx code - H530
haund - 384 freq
hummed - 10 freq
handy - 56 freq
haundie - 1 freq
haunt - 28 freq
hin't - 1 freq
haunit - 2 freq
hint - 78 freq
hoond - 2 freq
hand - 319 freq
hant - 6 freq
hunt - 62 freq
honed - 5 freq
him-it - 1 freq
hent - 6 freq
haundy - 18 freq
hannit - 20 freq
hantie - 4 freq
hamewith - 9 freq
hained - 27 freq
honey-dew - 1 freq
haunmaid - 1 freq
handee - 4 freq
handie - 3 freq
hind - 14 freq
haun't - 4 freq
hemmed - 3 freq
'haund - 3 freq
hem't - 1 freq
hannet - 2 freq
hummit - 1 freq
haand - 104 freq
hindu - 8 freq
him-hit - 1 freq
honeyed - 1 freq
hunda - 1 freq
haun-med - 1 freq
hainit - 6 freq
him-id - 1 freq
houmit - 1 freq
hunde - 1 freq
hinnied - 1 freq
hamada - 1 freq
haint - 3 freq
haunnit - 2 freq
hende - 1 freq
heymouthe - 1 freq
hynd - 1 freq
€œhunty - 1 freq
hound - 11 freq
€œhand - 1 freq
hnd - 3 freq
hindi - 2 freq
hond - 3 freq
henwudie - 1 freq
henwuddie - 3 freq
haun-made - 1 freq
hammett - 1 freq
heymooth - 1 freq
huntie - 1 freq
heynd - 1 freq
hmt - 1 freq
‘hand - 1 freq
hnuty - 1 freq
hmdt - 1 freq
honeat - 1 freq
hund - 1 freq
hnid - 1 freq
hunt' - 1 freq
handw - 1 freq
MetaPhone code - HNT
haund - 384 freq
handy - 56 freq
haundie - 1 freq
haunt - 28 freq
hin't - 1 freq
haunit - 2 freq
hint - 78 freq
hoond - 2 freq
hand - 319 freq
hant - 6 freq
hunt - 62 freq
honed - 5 freq
hent - 6 freq
haundy - 18 freq
hannit - 20 freq
hantie - 4 freq
hained - 27 freq
honey-dew - 1 freq
handee - 4 freq
handie - 3 freq
hind - 14 freq
haun't - 4 freq
'haund - 3 freq
hannet - 2 freq
haand - 104 freq
hindu - 8 freq
hunda - 1 freq
hainit - 6 freq
hunde - 1 freq
hinnied - 1 freq
haint - 3 freq
haunnit - 2 freq
hende - 1 freq
€œhunty - 1 freq
hound - 11 freq
€œhand - 1 freq
hindi - 2 freq
hond - 3 freq
huntie - 1 freq
heynd - 1 freq
‘hand - 1 freq
honeat - 1 freq
hund - 1 freq
hunt' - 1 freq
handw - 1 freq
HANNET
Time to execute Levenshtein function - 0.229296 milliseconds
The Levenshtein distance is the number of characters you have to replace, insert or delete to transform one word into another, its useful for detecting typos and alternative spellings
Time to execute Double Levenshtein function - 0.377905 milliseconds
In a stroke of genius, this runs the Levenshtein function twice, once without vowels and adds the distance together, giving double weight to consonants.
Time to execute SoundEx function - 0.029961 milliseconds
Soundex is a phonetic algorithm for indexing names by sound, as pronounced in English. The goal is for homophones to be encoded to the same representation so that they can be matched despite minor differences in spelling.
Time to execute MetaPhone function - 0.037889 milliseconds
Metaphone is a phonetic algorithm, published by Lawrence Philips in 1990, for indexing words by their English pronunciation.[1] It fundamentally improves on the Soundex algorithm by using information about variations and inconsistencies in English spelling and pronunciation to produce a more accurate encoding, which does a better job of matching words and names which sound similar.
Time to execute Manually curated function - 0.000897 milliseconds
Manual Curation uses a lookup table / lexicon which has been created by hand which links words to their lemmas, and includes obvious typos and spelling variations. Not all words are covered.