I have a machine learning challenge I may be over thinking. I have a set of 3.5 million products (not unique, there are multiple instances of each product). Each product has a "description" from it's manufacturer which is generally a long strong of a mixture of characters and words. For instance: as3200 wood xd9 dx-9078gl chisel .. it can be quite long (more than 1000 words/chars) You get the idea. In the past, humans have read the product description and then manually typed a name they thought was best. Sometimes "wood chisel" and sometimes " wood chisel as3200", sometimes "pointy wood thing you hit with hammer" (not kidding).
So this is a form of multiclass classification, but there are literally thousands of products. I'm looking for a way to learn the product names, not just classify them into categories. We've already done that with pretty good success.
Instead, we would like to have the algorithm read the description of the product and recommend the right product name. I have a training set of approximately 250,000.
I though this might be suitable for a recurrent neural network, but now I'm not too sure.
Or it could just be as simple as parsing out all the extra characters. Except, sometimes the product name is abbreviated or shortened in non systematic ways. Hence, humans are really good at applying meaning to ambiguous (but close) stimuli.
Any suggestions would be great.