How to solve most NLP problems: a step-by-step guide

Re:How to solve most NLP problems: a step-by-step guide by make3 on Thursday January 25, 2018 @12:47AM (#16229051)

I think you'd be surprised to find out that for a pretty large amount of classification problems, tf idf and Bayes naive classifiers do equally as good or almost as good but with less variance than deep nets.

also afaik fastext is just a faster way to build embeddings that are mostly equivalent to word2vec

All and all, I'm not sure you're enough of an expert to be commenting on this

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Re:How to solve most NLP problems: a step-by-step guide by nl on Thursday January 25, 2018 @08:07AM (#16230529)

1) No, they don’t. Eg, on the current Toxic comment classification TF-IDF and Bayes models are about 20% worse than RNN models.

2) FastText is also a classification algorithm, a command line classifier and a set of pretrained embeddings.

3) Yes, he does. I’ve learnt from his notebooks before, and I am confident I’m qualified to judge. I don’t completely agree with his statement here, but he is well qualified to comment.

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Re:How to solve most NLP problems: a step-by-step guide by make3 on Thursday January 25, 2018 @08:44AM (#16230681)

1) I'm saying, depends on the size of the datasets

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