Thanks!
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.
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.