Kotoba VS glossterm

Exactly and I don't know why more apps don't do this. I wonder if it's against App Store guidelines to make a dictionary app based on the built-in dictionary.

There are a lot of dictionary apps out there but as usual most developers miss the forest for the trees. I don't know why there's still not a single dictionary app that is blazing fast, use the built-in dictionary and have some common sense design choices (literally not came across a single dictionary app that doesn't enable keyboard as soon as you open it -- it's a dictionary app, why do they think I open the app?!).

Kotoba (https://github.com/willhains/Kotoba) is almost perfect but there's no way to download it from the App Store and I don't want to deal with the hassle of sideloading on iOS as a non-developer.

nice, thanks for info I realized that it would be even better to somehow take information from LookUp dictionaries and save to the app but I guess it will be not possible. I did some research and found this lib: https://github.com/willhains/Kotoba it uses dictionary API somehow to translate things, maybe this would help to get definition/translation for saved vocabulary to the app but in general things gets complicated here :D in ideal I would love to have an object of key (vocabulary) - value (translation) pairs and then export it to csv from there I found out that Quizlet app have feature to import from file it would be ideal way to fast generate flash cards to learn vocabulary

I saw a few posts discuss using the Wiktionary dump directly vs. the freeDictionary API, which is difficult to do because the raw wiki text isn't immediately usable. I actually created and open sourced a project several years ago that I never publicized that lexes and parses the Wiktionary dump:

https://github.com/vthommeret/glossterm

Specifically it can understand and execute 21 different wiki text templates (e.g. "cog", "borrow", "gloss", "prefix", "qualifier”), e.g. {{inh|es|la|gelātus}}:

https://github.com/vthommeret/glossterm/tree/master/lib/tpl

And eventually parse it into this structure, which has a list of all definitions (distinguished into nouns, adjectives, verbs, adverbs, etc...), etymology, links, and descendants for a given word:

https://github.com/vthommeret/glossterm/blob/master/lib/gt/p...

Further parts of the pipeline turned different relationships into edges that I could stick into a graph database and do certain graph queries. This allowed me to do certain queries like find French, Spanish, and English words that share a Latin root.

I ended up parallelizing this specific query using Apache Beam and then dumping the results into Firestore so they could be queried via a web app. Here's an example for the Spanish word: helado

https://cognate.app/words/es/helado

Under the "Cognates" section, it knows that it comes from the Latin root "gelatus" from which English has borrowed the word "gelato".

I originally started this project when I was learning Spanish. If you just look up the definition of helado (ice cream) it doesn't necessarily help you learn it. But I found that if I could relate it to languages I already knew (e.g. English and French), it was easier to remember. In this case helado is related to gelato, but you won't find that in e.g. Google Translate or SpanishDict.

Ultimately, I found that while the Wiktionary data is amazing, it’s also a bit of a quagmire for finding cognates. I would miss certain etymologies where you had to follow a descendant tree 2 or 3 levels deep. Or a definition would just mention a word it was related to. But if I expanded the query to include these instances, then it significantly increased the amount of non-cognates that showed up in the results.

So I created a useful set of tools (which I never wrote about until now), but I realized the end result of a web UI that showed the relationships between words would require a significant investment in data quality that likely wasn’t possible without changing Wiktionary itself / community investment.