Introducing Niai: Lookup similar Kanjis, Homonyms, Synonyms

I started working on this out of a serious need. I went about collecting various dictionaries in the wild and then aggregating data.

http://niai.mrahhal.net

Niai is a web app with 3 main features:

  • Lookup similar Kanjis

Type any amount of kanjis you want to lookup, the result will come back with a list of similar kanjis per each kanji. An example with 「枝方寄」:

The inspiration for this is @acm2010’s script

  • Lookup Homonyms

Those are words with same readings but possibly different meanings. An example with 「かえる」:

  • Lookup Synonyms

Simple enough, those are words sharing similar meanings. An example with “affection”:

This started as an experiment here: A tool to show useful aggregated data based on your WK level (homonyms, frequency, part of speech, ...)


A dark theme is also available!

Switch the theme from the button in the bottom left corner.

Kanji cards contain links to the WK level it’s introduced at, as well as a quick link to Jisho. Kanji and Vocab cards also display (and are ordered by) the frequency of the character or term (this is obtained from Innocent Corpus).


I also publish a swagger document for the api: http://api.niai.mrahhal.net/swagger
You’re free to use it however you like. Right now there’s no versioning but I’ll try to keep it backward compatible.

The whole thing is open sourced on github: https://github.com/mrahhal/niai. Any suggestions/contributions are very welcome!

37 Likes

Awesome stuff! I think you forgot to add the link to the actual site, though. :wink:
http://niai.mrahhal.net/similar

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Oh god :joy: Thanks

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Thanks for the site, I’m bookmarking it as I always confuse something :eyes:

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Great work and thanks for sharing! I needed this for my Kanji journey and it’s always fun to check similarities and homonyms and what not

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Thank you!!

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Thank you so much!

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This is really impressive, did you enter the data yourself? If so then wow that must’ve taken a lot of your time

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Thanks but I definitely didn’t heh. That would take me many lifetimes. I used several dictionaries and then aggregated the data into several feature categories. (The tool that does that is open sourced in the repo)

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