3 ms·
Various NLP tasks used to be very heavy on symbolic approaches (with some of them still being used), I myself worked on them for some years until the statistica
by PeterisP 5y ago
Various NLP tasks used to be very heavy on symbolic approaches (with some of them still being used), I myself worked on them for some years until the statistical approaches started to work better. For computer vision, I would probably consider the work on edge detectors, HOG and SIFT algorithms as the "symbolic" heritage for object detection which has now been replaced with pure ML.
- YeGoblynQueenne 5y agoNLP has been dominated by statistical learning approaches for some time, that's true, although when I did my Master's in 2016, I seem to remember the Brill tagger was still considered state-of-the-art and there was still a lot of work on learning PCFGs or dependency grammars. Perhaps that just happened to be what the tutors at my course were working on, though. In any case, it seems to me that while real progress has been achieved in language modelling, the same cannot be said for language understanding. That's a bigger conversation but anyway, modelling is still what statistical learning techniques do best, whereas anything to do with semantics, you still need some kind of symbolic approach. I never thought of HOG and SIFT as "symbolic". If I remember correctly, they were just sets of hand-crafted features? But, features for classifiers, like SVMs and so on.