Defining the Context Layer in NLG. Part 1: What Just Happened


In my last blog post, I outlined the differences between good Natural Language Generation and bad NLG. My conclusion was that all of the hard work and sweat should be going into defining the NLG context layer, the logical decision matrix that sits between the data and the words.

This is because NLG content is less about the words and more about the insights, the data. The context layer will help make sure the words are used to convey the data to the reader in a manner that can be absorbed quickly and easily.

So how do you define a context layer?

First and foremost, defining the context layer is a business process, not a technical process. It's akin to defining business requirements for custom software, and then translating those business requirements into technical requirements. The beauty of Wordsmith is that it allows anyone, regardless of technical aptitude, to translate their own business requirements into automated content.

But like any business initiative worth pursuing, just because the process isn't technical doesn't mean it isn't complex. In fact, when we work with customers to assist them in creating massive automated content projects, like Yahoo's Fantasy Football Recaps or the AP's Quarterly Earnings Reports, we spend the vast majority of our time working with our customer to define the context layer, and we take most of our cues from their industry experts.

The goal when creating good automated content is to unlock the story hidden within the data. And those stories can usually be boiled down to a few basic structural elements.

In this post, we'll look at the first and most important of those structural elements, and talk about building a context layer around it.

read the rest at: