Word Frequency
Counts how often each word or n-gram appears, with Korean-aware tokenising and a stopword list.
Counts how often each word, character or n-gram appears, with the Korean tokenisation stated rather than assumed. Korean spaces separate 어절 — a stem with its particles attached — not words, so 서울에서 and 서울은 count as two different tokens unless you ask otherwise.
Text
Counting is a single pass over the text and runs on a 200 ms delay after you stop typing.
How to count
2 counts pairs of adjacent words.
24 distinct terms
tokens counted
29
distinct
24
used once
21
hapax legomena
type-token ratio
0.828
falls as a text lengthens
- 1the413.8%
- 2meeting26.9%
- 3seoul26.9%
- 4복잡합니다13.4%
- 5서울의13.4%
- 6시간에는13.4%
- 7지하철은13.4%
- 8출퇴근13.4%
- 9편리하지만13.4%
- 10agreed13.4%
- 11and13.4%
- 12any13.4%
- 13at13.4%
- 14busy13.4%
- 15hour13.4%
- 16in13.4%
- 17is13.4%
- 18long13.4%
- 19on13.4%
- 20plan13.4%
- 21ran13.4%
- 22team13.4%
- 23well13.4%
- 24went13.4%
How to use it
Choose the token
Words as spaced is the honest reading of the text. Particles trimmed makes 서울 rank as one word. Characters and n-grams are for looking at the shape of the text rather than its vocabulary.
Drop the common words if they crowd the top
English and Korean stopword lists, separately or together. Without them the top of any list is the, and, 그리고 and 하지만.
Set the phrase length for n-grams
2 counts pairs of adjacent words, which is how you find the phrases a document actually repeats rather than the words it happens to use.
Common questions
Why do 서울에서 and 서울은 count separately?
Because they are different 어절, and by default the tool counts what is actually in the text. "Particles trimmed" strips a trailing 은/는/이/가/을/를/에서/으로 when at least two syllables remain, which makes them one.
Is the particle trimming reliable?
It is a heuristic, not a morphological analyser. It will occasionally trim a noun that genuinely ends in one of those syllables, and it does nothing about verb conjugation — 갑니다, 갔다 and 가서 stay separate.
What is the type-token ratio for?
Distinct words divided by total words — a rough measure of vocabulary variety. It falls as a text lengthens, so it only compares meaningfully between texts of similar length.
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