It has been mentioned before, but not in the request section. The parsing of Chinese subtitles needs a LOT of improvement. It’s almost unusable for saving vocabulary and keeping track of known words or phrases. Many of the characters grouped together as “words” aren’t in any real dictionary (I don’t know what LLN is using as a dictionary, but these words aren’t in any of the many reputable PLECO ones).
Suggestion: Would the technology behind “Zhongwen Chinese Popup Dictionary” extension be useful? It is open source on GitHub. It seems to do a good job parsing and gives the range of shortest and longest viable character chunks that could be considered words or phrases (even some chengyu). You could limit parsing to only to words in the the open source CC-CEDICT dictionary or dictionary of choice.
So depending on which character in a string you click/hover, you will have the option to save and get every definition. For example: 在後面 can be parsed into the words: 在 後 面, 在 後面, or 在後 面. The latter may not make sense in context, but they are real words. If you hover or click on the 後, you should have the option to see and save the definition for both 後 and 後面. If you click on the 在, you should have the option to see and save the definition for 在 and 在後. It will also eliminate a lot of non-sensical groupings that LLR is currently giving. This will cause some color coding conflicts, but that could be resolved by prioritizing certain colors over others where there is overlap and/or by longest dictionary approved character string.
I have been color coding all the words I know and am learning different colors so I can tell at a glance whether I should really work at 100% understanding a sentence or not, or let my brain rest. This inaccurate parsing is definitely a headache. A lot of the “words” aren’t even words. I can’t even save many common words because they are parsed separately or grouped with unrelated characters.
That said, LLR has been invaluable for my learning, so thank you so much for your work so far! It is the only reason I even subscribed to Netflix because of all the soft subs. I just joined the Pro version because color coding what I know, what I’m learning, and what I don’t need to worry about is making a huge difference in learning efficiency. I hope you can make these parsing changes soon to make it even better.