
NLP-progress: Tracking Progress in Natural Language Processing - sndean
https://nlpprogress.com/
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groceryheist
I am a little worried about the accuracy of sentiment classifiers, relative to
how often I see them used and in applied research settings. They work well for
binary classification on Tweets and IMDB reviews and not much else (I guess
they do alright at 93% if you magically have a parse tree).

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Radim
It's not just sentiment classification. Many of these NLP methods overfit to
the minutiae of the particular SOTA data set and data type.

Language identification, NER, POS tagging, parsing, tokenization… you name it.
Solved in theory, trouble in practice.

Using the algos on a concrete project, with a concrete "upstream" goal, is the
only way to assess the gap between SOTA and your needs. Don't blindly trust
these 90+% scores.

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mark_l_watson
Great resource. How times change: I didn’t see any non-deep learning systems
in this list. I had been out of the field of NLP for a while and then went to
NAACL 2016 - very surprised to hear all of the deep learning papers.

