Deep Learning for NLP, advancements and trends in 2017

Deep Learning for NLP, advancements and trends in 2017 by Javier Couto.

If you didn’t get enough books as presents, Couto solves your reading shortage rather nicely:

Over the past few years, Deep Learning (DL) architectures and algorithms have made impressive advances in fields such as image recognition and speech processing.

Their application to Natural Language Processing (NLP) was less impressive at first, but has now proven to make significant contributions, yielding state-of-the-art results for some common NLP tasks. Named entity recognition (NER), part of speech (POS) tagging or sentiment analysis are some of the problems where neural network models have outperformed traditional approaches. The progress in machine translation is perhaps the most remarkable among all.

In this article I will go through some advancements for NLP in 2017 that rely on DL techniques. I do not pretend to be exhaustive: it would simply be impossible given the vast amount of scientific papers, frameworks and tools available. I just want to share with you some of the works that I liked the most this year. I think 2017 has been a great year for our field. The use of DL in NLP keeps widening, yielding amazing results in some cases, and all signs point to the fact that this trend will not stop.

After skimming this post, I suggest you make a fresh pot of coffee before starting to read and chase the references. It will take several days/pots to finish so it’s best to begin now.

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