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hpr4026 :: Using NLP to get better answer options for language learning

Levenshtein distance may help language learning apps improve answer options for better learning.

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Hosted by thompsgj on 2024-01-08 is flagged as Clean and is released under a CC-BY-SA license.
levenshtein, nlp, language learning, education. 2.
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Duration: 00:16:34


Code snippets

Install the Levenshtein package.

>>> pip install levenshtein

Use the Levenshtein package to calculate the edit distance.

>>> import Levenshtein
>>> Levenshtein.distance("cat", "dog")
>>> Levenshtein.distance("cat", "can")

Install the TheFuzz package.

>>> pip install thefuzz

Use TheFuzz to calculate the similarity ratio.

>>> from thefuzz import fuzz
>>> fuzz.ratio("cat", "dog")
>>> fuzz.ratio("cat", "can")

Use TheFuzz to calculate the similarity ratio for each item in a list.

>>> from thefuzz import process
>>> options = ["dog", "can"]
>>> process.extract("cat", options, limit=2)
[('can', 67), ('dog', 0)]
>>> process.extract("cat", options, limit=1)
[('can', 67)]
>>> process.extract("cat", options, limit=2, scorer=fuzz.ratio)
[('can', 67), ('dog', 0)]

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Comment #1 posted on 2024-01-10 16:39:22 by norrist

Interesting Topic

Great First Episode. I like how you explain the complex topic.

Comment #2 posted on 2024-01-14 16:48:02 by Dave Morriss

Levenshtein distances

Interesting show!

I thought I'd heard of Levenshtein distances before, as I was listening. Later I remembered that in the university I worked in (I retired 15 years ago), I added this feature to our OpenLDAP directory server. It allowed us to offer a "sounds like" search capability, though it might have been specific to OpenLDAP (I don't think it's a standard capability).

The LDAP server was used by the university's web-based search tool, amongst a whole load of other services.

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