This is a big part of why language learners have largely moved toward sentence mining as the preferred way to build an Anki deck.

Getting your words from real-world contexts, and keeping that context on the front of the card, largely eliminates the ambiguity problem. If a word has multiple senses, it gets multiple cards with different example sentences to illustrate each one.

It also helps a bunch with words that don’t really have a concise translation to your native language. For example the French words “mur” and “paroi” both mean “wall” in English, but the contexts where you use them are quite different. An example sentence helps with that, and getting that sentence from an even richer context such as a book or article you’ve read helps even more.

It’s also, frankly, just more enjoyable. I’ve come to view frequency lists as an antiquated tool. I needed them in the 1990s when good authentic-context study materials were hard to come by, but the modern Internet has made so-called immersion-based learning methods so easy and inexpensive I’m frankly mystified that people still cling to the joyless, almost mechanistic methods we were stuck with in the previous century.

Thank you, its good to hear some of what the state of the art is. My natural language processing studies at university are around the vintage you mention. I will have a go at this...