Recently I had to build an algorithm that did a form of substring detection.  As part of this process I had to generate feature vectors for the actual model to classify tokens of the string as part-of-substring or not.  Prior to this though, I had to do a few levels of tokenization, normalization, and preprocessing, to get the raw text into a form that the substring classifier could use the resultant data effectively.<p>Ideally if you have a good parse of the text from step 0, you don&#x27;t need to do nearly as much munging&#x2F;processing yourself, and can just focus on the thrust of your specific algo, rather than cleaning and generating the feature data to drive it.<p>I&#x27;m personally very skeptical as to if this will make feature engineering disappear entirely, if for no other reason than that we do a lot to tweak our features other than just _getting_ them, whether this be second order processing, aggregation, smoothing or transformation.  That being said, as with your parent post, I am VERY hopeful for new techniques to arise that can cut into that overhead at least a little.