It’s no secret that quality content is of utmost importance if you hope to rank on page one of the search engine results pages (SERPs) and boost your on-site conversions. Producing quality content has always been a laborious task, but technology is lending a helping hand. Natural language understanding (NLU) and generation (NLG) has been proven to assist in this particular activity and the results are remarkable. Hamlet Batista shared his expert opinion on this subject in his presentation at the SEJ eSummit and just last week he published an article expanding on his idea of automating content generation. Batista has presented his findings on generating FAQs and FAQ page Schemas using the Python programming language.
Let’s back up a little and consider the relationship between NLG, machine learning, deep learning and Google. Search engines’ main function is to serve up relevant URLs that answer their users’ questions. In recent years, search engines (in particular Google) have begun serving up quick and easy-to-understand answers ahead of the URLs that provide the in-depth corresponding content. Think Featured Snippets, People Also Ask accordion content and the Knowledge Graph to the top right of the SERPs. There’s been some serious controversy around these features in recent times, but with user intent high on everyone’s agenda, webmasters and site owners are working hard to ensure their content is focused on user intent. Ultimately, online brands are fighting to be showcased in these features. All the while, Google has been working on NLG and has adopted this technology across its platforms.
Have you noticed your sentences you type into Google Documents, on Hangouts and in your emails are autocompleting and it’s accurate to what you’re trying to communicate?
This technology is not isolated to Google, though. It can be adopted and used across a multitude of platforms. Batista’s point here is that automated content is likely the next technology to be adopted by SEO professionals and webmasters. Why? Because creating high-quality content to nab the featured snippet real estate on the SERPs takes up too much time and resources. Also, because the content written most likely already exists, but as proprietary knowledge that is not available to the public as it exists in private client emails, chats and intranet-type platforms. This knowledge, however, can be leveraged by adopting “transfer learning” technology. This is within the realms of machine learning but is considered deep learning. Instead of simply working with existing and already-known information, it combines it with the hidden information. Through deep learning technology, it taps into the common sense and logic side of machine learning. So we edge ever closer to artificial intelligence …
In this article and in his presentation, Batista explains how to automate content generation. But in his article from last week, Batista forgoes the idea of identifying and sourcing the questions (intent) of the users for content generation. Instead, he uses Python to automatically create question/answers pairs and the necessary schema — resulting in high-quality FAQs content and FAQs schemas. It’s pretty groundbreaking considering a website’s FAQ page is incredibly important to its user and to its SEO and content marketing strategy.
His technical plan to make this possible is laid out in his article exactly as follows:
Batista offers up practical tips and tricks on tweaking code at various stages in the automated creation of the FAQs and the FAQs Schema. He identifies every tool he has used in this build and has included images to support his claims.
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