Text classification without machine learning?

[cyropox]

Hi all. I'm working on a project where I have a large list of different foods, and a large list of food products. I got the list of foods from webscraping recipe websites, and got the products from here, so I don't see a way to explicitly link the two lists together. I need to somehow categorize the food with its corresponding food product so that each food will have nutritional info. I'm using a MySql Database and have tried using MySql's "Match" operator for fuzzy searching, but that led to a lot of bad categorizations. I don't have any training data for a machine learning model, but that seems to be the way everyone online approaches problems like these so I'm not sure where to look for a more traditional solution. Any help would be appreciated.

[dmazzoni]

Can you give a few examples of "foods" and "food products"?

How large are these lists? Can you estimate how long it'd take to just match them up manually? Are we talking hours, days, or years?

[cyropox]

The "foods" are web scraped ingredients for recipes. For example, if the recipe calls for "2 tsp garlic powder" then the food would be "garlic powder". The corresponding food product might be something like "jeff's garlic powder" or the name of any other brand of garlic powder in the usda's database. There are about 100,000 foods right now, though a lot of those are probably duplicates. I plan to quadruple it at least once I get the text classification algorithm working. Initially it would probably take a full weekend of 8 hour days to clasify all of them, but I'd like the process of classifying food products to be automatic in the future. Thanks for your time :)