> I think there is no fancy NN (yet) behind Google search,
During the deep learning boom, Google made a huge push towards NN-based NLP. SEO's and their PR calls their efforts collectively RankBrain: https://en.wikipedia.org/wiki/RankBrain
I think we are on the cusp of combining symbolical/logical operations over the vectors produced by Neural Networks (or at least, major effort there). Could be by neatly tying up all these different NN-based NLP modules (parsing, semantic distance, knowledge bases, ...) with another set of decision layers stacked on top.
Doing just that for 10 years, beating hand-coded systems: https://www-nlp.stanford.edu/pubs/SocherLinNgManning_ICML201... [pdf]
> I would guess that most modern NNs from the NLP area (Transformer or LSTM) would be able to correctly differentiate the meaning.
Yes. See demos like: https://demo.allennlp.org/constituency-parsing/MTczNjYyNA== and https://demo.allennlp.org/dependency-parsing/MTczNjYyNg==
> I think there is no fancy NN (yet) behind Google search,
During the deep learning boom, Google made a huge push towards NN-based NLP. SEO's and their PR calls their efforts collectively RankBrain: https://en.wikipedia.org/wiki/RankBrain
I think we are on the cusp of combining symbolical/logical operations over the vectors produced by Neural Networks (or at least, major effort there). Could be by neatly tying up all these different NN-based NLP modules (parsing, semantic distance, knowledge bases, ...) with another set of decision layers stacked on top.