alt Hacker NewsThanks! As far as I understand your idea is to starts from the word and pulls examples from some huge data source. My approach is the other way round: I start from a source (the audio that you want to learn), and the tool extracts only the words that appear in it, with their meaning in that context. I think that hugely simplifies the implementation, and it is more useful for learners. They learn the meaning in a particular context.
As for the stack: STT with Soniox (word level timestamps), then spaCy for segmentation, POS and lemmas, then AI enrichment, correcting the lemma when spaCy is wrong. Some languages have no spaCy model at all and others are unreliable. I am trying to do spaCy thing in LLM then. Plus some extra magic for Japanese and Chinese.
Awesome, and yes, totally makes sense -- you are more learner-centric that way.
Having the full sentence context is actually one of the things I have been thinking about a lot -- this helps both the learner as well as the POS detection in Stanza. I always decided against, because I wanted to build agnostic flash-cards.
However, as your approach allows on-the-fly generation of flash cards, you always stay close to the learner progress. I could (e.g.) pick some Gutenberg fairy tales, allow the learner to read them in their target language and provide bi- and omni-directional translations across all languages. Creating flash cards from the source material keeps the learner in progress (context), allows to learn new words step-by-step (discovery), as well as providing a fun learning experience and measurable progress. Similarly, instead of fairy tales, we could use some series in combination with its subtitles. This allows video-progress. Awesome x2!
Sidenote: The awesome part about HN is that I get to chat with like-minded people and directly grasp some new inspiration. Probably I ought to visit some in-person hacker spaces :)