While there are many abilities that contribute to our intelligence, it’s our ability to communicate thoughts and ideas by means of a language that succeeds them all. Language is our user interface as we build language to convey our thoughts, and send them across using our voices, keyboards or gestures if we are using sign language. Whichever the means, it is important that the recipient understands our thoughts just the way we would want them.
What if we could build systems that not just spoke like humans but also answered human questions in human language?
Natural Language Generation is a way to achieve just that.
Natural Language Generation (NLG), a branch of artificial intelligence (AI) which generates language as output on the basis of data as input. There has been significant rise in adoption of NLG into business, in recent times. With NLG driven Robo-journalism and Robo-advisory taking the manual reporting arena by a storm, you can be sure that you must have read machine written articles, only without realizing it. Yes, they are as good!
As humans, we always tend to communicate ideas from data. But with the recent magnanimous influx of data that needs to be analyzed and interpreted, that too with ever increasing pressures to contain costs and meet dynamic customer demands, the business must find innovative ways to keep up. As it turns out, a machine can articulately communicate ideas from data at remarkable scale and accuracy. When a machine automates the regular mundane routine analysis and communication tasks, productivity increases and employees can focus on decision making and end actions.
The goal of NLG systems should be to understand how to best communicate what it knows. For that it needs to have an unbiased and clear picture of the world rather than random strings of text. Simple NLG systems are capable of taking in ideas in form of data and transforming them into language. Apple’s Siri uses this concept of linking ideas to sentences to in turn produce limited yet succinct response.
Based on its extent, Natural Language Generation can be classified in three types – Basic NLG, Template driven NLG and Advanced NLG. The simplest level or basic level of NLG would identify and gather few data points and transcribe them into sentences. For e.g. a simple weather report like this: “the humidity today is 78%.” The next level of NLG, also known as template driven NLG, as the name suggests, uses template heavy paragraph to generate language as per the dynamic data. Sports score chart, basic business reports can be made using this type of NLG. Here, language is generated by the virtue preliminary business rules guided by looping statements like if/else statements. Advanced NLG is the artificial intelligence that can convert data into a narrative with distinct introduction, elaboration and conclusion. Deepest analytics are applied to execute this form of NLG. vPhrase’s PHRAZOR is one such advanced Natural Language Generation platform that generates elaborate narratives be it in sports or finance, as per the end user’s requirement.
Coming to business, you’d now wonder how exactly it would help an organization.
When you use Natural Language Generation, you can assemble more big data and by assembling more big data, you gather even more number of critical data points resulting in more insightful information to sell and pass across; thereby working toward increase in your revenue. With NLG, you can communicate insights at faster and larger scale as compared to manual efforts, increasing the overall analytic productivity of the organization. NLG, if not eliminate, can significantly reduce time-consuming and exhaustive data analysis, and manual reporting, resulting in increased operational proficiency. Additionally NLG would enable you to deliver customized, updated, data driven and simple information to all the customers as per their needs.
Big Data is here to stay and so it’s up to us to keep up with technology to harness it, and Natural Language Generation is one such tool that empowers us with utilizing this massive data while not letting our creative energies dry out in the mundane data transformation processes. Keep your intelligence reserved for decision making and action planning; and make way for Robo-writers!