This post originally appeared at TheGradient and was edited by Andrey Kurenkov, Eric Wang, and Aditya Ganesh. Big changes are underway in the world of Natural Language Processing (NLP). The long reign of word vectors as NLP’s core representation technique has seen an exciting new line of challengers emerge: ELMo, ULMFiT, and the OpenAI transformer. These works made headlines by... Read more

This article is the second article in a two-part series about the history of NLP as told through the context of five research papers. It picks up in midst of the 1970s. To view the first article, click here. Corpus resource development The relation-driven academic era that spilled into... Read more

A while ago I posted about how to use machine learning to understand brand semantics by mining Twitter data — not just to count mentions, but to map the similitudes and differences in how people think about them. But individual tweets are brief snapshots, just a few words written and posted... Read more

With advances in computational power and the integration of artificial intelligence, the natural language processing domain has evolved into a whirlwind of innovation. In fact, experts expect the NLP market to swell to an impressive $22.3 billion by 2025. In the meantime, here’s a look at three NLP trends... Read more

Introduction Natural language processing (NLP) exists as a sort of intermediate space between computer science and linguistics, taking more from either discipline as fit for the task at hand. With this breadth in mind, NLP ends up serving as an umbrella that encompasses everything from named entity recognition to... Read more

Since a few years, chatbots are here, and they will not go away any time soon. Facebook popularised the chatbot with Facebook Messenger Bots, but the first chatbot was already developed in the 1960s. MIT professor Joseph Weizenbaum developed a chatbot called ELIZA. The chatbot was developed to demonstrate the... Read more

The world of the C-suite isn’t quite ready to embrace artificial intelligence just yet. That’s according to an oft-cited Accenture study from late 2016, which found that less than half of the 1,700 business leaders interviewed in a global sample would feel comfortable trusting the advice of AI “in... Read more

Imagine you’re Rene Descartes, data scientist at Facebook in charge of saying true things. Problem is, your only input is what people say, and everybody lies. It’s not a moral failure, it’s just that you’ve built Facebook into the world’s largest integrated content distribution machine, and convincing people of things... Read more

Note: This post was written together with the awesome Julian Eisenschlos and was originally published on the TensorFlow blog. Hello there! Throughout this post we will show you how to classify text using Estimators in TensorFlow. Here’s the outline of what we’ll cover: Loading data using Datasets. Building baselines using pre-canned estimators.... Read more

The rise of Big Data created the need for data applications to be able to consume data residing in disparate databases, of wildly differing schema. The traditional approach to performing analytics on this sort of data has been to warehouse it; to move all the data into one place... Read more