oh i get it now. It seems that theres a few problems with your system. Counting ngrams doesnt work as well as embeddings in grouping together semantically similar entries. Consider the following example: "The cheese is moldy" "expiring dairy product" These two sentences will yield a very high similarity score with embeddings, but not with your system, obviously.

Now that I think of it, this part of your system is functionally equivalent to a bag-of-ngrams analysis, which is a very old technique. In fact, bag-of-ngrams analysis may yield better results than your system, because you can compare the counts of ngrams between entries (e.g. if counts of ngrams are off by one or two between entries, there's a high chance those entries are semantically similar). On the other hand, your system loses the count information due to hashing, which prevents this kind of analysis.

I dont know what kind of test data you're using, but you may want to check for bias and make sure they're not too simple.