People once believed that once a computer had beaten a human player at chess, machine intelligence would have surpassed human intelligence. But mastering chess for a computer turned out to be comparatively trivial, as demonstrated more than two decades ago when IBM’s Deep Blue beat Garry Kasparov, the former world chess champion.
And although machines have certainly surpassed human performance in some domains, teaching computers how to communicate via human language, something we do with little effort, has remained a challenging task.
However, owing in part to developments in algorithms and the democratization of natural-language processing (NLP) in the Python community, recently the field has seen rapid advances. What follows is an overview of the most popular NLP applications and techniques with practical implementations in Python.
Have you ever marveled at Google’s ability to find the right answers to even poorly formulated search queries? Or maybe you’ve been astonished by the ever-increasing accuracy of its machine translation service?
In this article, we’d like to cover some of the innovations that Google has contributed to the field of natural language processing (NLP). We’ll introduce you to its cloud-based NLP services and show you how to use them in your own projects.
The internet is overflowing with resources for learning new programming skills. For thriving disciplines like natural-language processing (NLP), you can find plenty of tutorials, video series, and university lectures online. All of these formats can be great ways to get you started. But when you want to get a truly deep understanding of a new topic, nothing beats a good book. For this article, we’ve compiled a list of our all-time favorite books that you should have in your pack before embarking on your NLP journey.
Each year, AI practitioners compete for the Loebner prize. This is an implementation of the Turing test where a computer‘s “humanness” is assessed by a panel of judges. The machine passes the test if it manages to convince the judges that it – and not its human competitor – is a real person. How does it do this? Simply by using language. Human-like conversation is but one of the many applications of Natural Language Processing, NLP for short.