Machine Learning Pioneers a New Generation of Technical Writing Solutions

My colleagues and I at Smart Data Collective have written extensively about the benefits of big data in fields like marketing, hospitality and cybersecurity. We sometimes realize that we need to discuss the implications of big data for other fields as well. Technical writing is one field that is highly affected by advances in big data, but does not get discussed very often.

How does machine learning influence technical writing?

The technical writing profession predates the concept of machine learning by nearly a century. However, it has evolved with trends in big data. A number of new help authoring tools are relying on big data. Here are some of the reasons that technical writers need to evolve with new developments in machine learning.

Machine learning is helping writers in every niche perfect their grammar

Every writer needs to fine-tune their grammar. Readers expect the words on their screen or pages of their pamphlets to be reasonably free of copy errors. However, some writers struggle more in this area than others. Technical writers often come from an engineering or technical support background. They are not writers by profession, so they might not understand all of the grammatical rules of the English language. This is one of the most important ways that machine learning is helping. The most dependable grammar checking tools, such as Grammarly use complex machine learning technology. This technology is able to review large blocks of text and identify grammatical errors. This might not seem like a very novel development. Spelling and grammar checking the tools have been around for around 20 years, long before the concept of machine learning and big data was widely available. However, machine learning is significantly improving the performance of grammar checking tools. One difference is that these tools pay attention to the accepted changes that users make. They might realize that certain proposed changes are frequently ignored by the writers using these tools. The machine learning algorithms that are built into them pay attention to this. They will eventually learn that these suggestions were not very helpful. As a result, they will start offering more helpful solutions. Since technical writers are at a disadvantage when it comes to checking grammar, machine learning grammar checking tools make a huge difference.


Machine learning could help create templates for certain guides and copy

When you are creating copy for technical guides, you will often find that a lot of the content is similar. You still need to make tweaks over time, but you can reduce the work involved by having a general template in place. The problem is knowing which template to use. You might need dozens or hundreds of templates, depending on the range of technical papers that you work on. Of course, since you are always going to need to make alterations to your copy, finding the right template is very important. Machine learning technology can assist with this. You can access a database of thousands of technical guides. The machine learning algorithm can look for similarities between your initial content and previous technical weight papers and tutorials. Then it will be able to provide the best template for you to use.

Finding data to add to your content

This is possibly the most important benefit of using machine learning for technical writing. You?re going to need to use statistics and empirical data to support a lot of your points. Machine learning technology is able to search all known data sets for this information. These algorithms can then relay relevant information for you to incorporate into your whitepaper.

Big Data is Changing the Writing Profession

Being a technical writer is not easy. Machine learning is having a profound impact on the field. You can use machine learning technology in a number of ways to make your technical writing more effective.



Rehan is an entrepreneur, business graduate, content strategist and editor overseeing contributed content at BigdataShowcase. He is passionate about writing stuff for startups. His areas of interest include digital business strategy and strategic decision making.