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I am stuck with the idea of developing a deep learning model to find news updates for specific topic let's say invention in "autonomous driving". I have some articles labeled by experts. (label relevant/not relevant). I would like to have a contentwise similarity score for new articles on the web.

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  • $\begingroup$ So what is your question? $\endgroup$ Nov 27, 2019 at 9:39
  • $\begingroup$ He doesn't know where to start. $\endgroup$ Nov 27, 2019 at 11:33

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NLP is rather quite hyper-dimensional. I'd go data-driven way and use some pretrained embedder. Nowadays there're a few to choose from, like LASER from Facebook. There's unofficial pypi lib, though it works just fine. If you want to reach seminal-like scores, there's no point in doing NLP by hand. Embedders usually cover dozens of languages, so you can feed training data in any language you want. Your models will also work for those languages out-of-the-box, even if you trained them on other languages. If you need some custom stuff, you could pick BERT from Google, though you'll have to push it yourself further. It isn't really pretrained that much.

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  • $\begingroup$ Thank you very much.I will try using LASER $\endgroup$
    – pyBug
    Dec 5, 2019 at 12:20

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