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Term clustering, by measuring the string similarities between terms, is known within the natural language processing community to be an effective method for improving the quality of texts and dictionaries.
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You can also square the corners by measuring the strings.
Legend has it that he used to stretch string between two points and then measure the string, translating inches into a flying boat's hours in the air.
Then measure the string with a ruler to find the circumference.
It's best to measure the string on your head before cutting it.
Mark the string off where it circles back around, and then measure the string length with a ruler.
You'll need something to measure the string with later, such as a ruler or tape measure.
You should measure the string to be able to wrap around your wrist at least twice -- having longer string will make it easier for you to comfortably braid the bracelet.
In this context, the edit distance (Levenshtein distance) is relevant to measure the strings similarity.
Dig the entire area to the same depth, frequently measuring the depth from the string to the base of what will be the rain garden.
Instead of determining the similarity between two strings measuring the fraction of common k-mers (i.e. small contiguous subsequences), we determined here the similarity between two graphs by counting the shared fraction of a special type of compact subgraphs, called neighborhood subgraphs.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com