While there are many abilities that contribute to our intelligence, it’s the ability to communicate thoughts and ideas by means of a language that succeeds them all. We use language to convey our thoughts via various mediums, and irrespective of the medium, it is important that the recipient understand our thoughts just the way we would want them to.
In the business landscape too, communication becomes easy when done using plain natural language. And what if we could build systems that not just spoke like humans but also answered questions in human language?
Natural Language Generation (NLG) is a way to achieve just that.
As humans, we always tend to communicate ideas from data. But with the recent influx of data that needs to be analyzed and interpreted, that too with ever-increasing pressures to contain costs and meet dynamic customer demands, businesses must find innovative ways to keep up. So what is Natural Language Generation (NLG)?
Natural Language Generation (NLG) is a branch of Artificial Intelligence (AI) that generates language as an output on the basis of data as input.
There has been a significant rise in the adoption of NLG into business, in recent times. 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.
According to a report by Gartner, it is predicted that by 2022, 25% of enterprises will use some form of natural language technology. 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 the form of data and transforming them into language.
Apple’s Siri uses this concept of linking ideas to sentences to in turn produce a limited yet succinct response. Another real-world example of NLG is robot journalism, which automates the process of reporting and content writing by producing comprehensive articles written in plain natural language, based on input data.
Types of NLG
Based on its extent, Natural Language Generation (NLG) in AI can be classified into three types – Basic NLG, Template-driven NLG, and Advanced NLG.
1. Basic NLG
The simplest level or basic NLG would identify and gather a few data points and transcribe them into sentences. For example, a simple weather report like this: “the humidity today is 78%.”
2. Template-driven NLG
The next level of NLG, also known as template-driven NLG, as the name suggests, uses template-heavy paragraphs to generate language as per the dynamic data. It relies on hard-coded rules with canned text, placeholders and special data representations. Here, language is generated by the virtue of preliminary business rules guided by looping commands like if/else statements. Sports score charts, stock market updates and basic business reports can be made using this type of NLG.
3. Advanced NLG
Advanced Natural Language Generation tools are more flexible than basic and template-driven NLG. It uses Machine Learning to convert data into narratives with a distinct introduction, elaboration, and conclusion. Deep learning neural networks that learn lexical, morphological and grammar patterns from written language are applied to execute this form of NLG.
Phrazor- an augmented analytics tool uses Natural Language Generation technology to generate elaborate narratives be it in sports, finance, or pharma as per the end user’s requirement. It also features report templates built for a wide range of use cases which can help business users to generate relevant and actionable insights from business data within minutes. Check out the below video to know how the tool works.
Natural Language Generation Use Cases
Natural Language Generation can be of great utility in Finance, Human Resources, Legal, Marketing, Sales, Operations, Strategy, and Supply Chain. Industries such as Financial Services, Pharma & Healthcare, Media & Entertainment, Retail, Manufacturing and Logistics can benefit from this technology to a great extent.
Some of the most common NLG examples and applications are as follows:
Enterprises across industries use NLG for report generation. NLG-powered Business Intelligence tools can analyze data to derive actionable insights and convert them into comprehensible reports. Natural Language Reporting can convert piles of data charts and complex graphs into concise insights in plain natural language. Business leaders can use these insights to easily come to conclusions and make effective decisions that would save them a lot of time.
NLG can be used to automate content creation by coherently putting long sequences of sentences together and creating personalized content. This technology can help businesses to create content for internal communications, product descriptions, agreements, business reports, contracts, and similar forms of textual communication. Automating manual writing reduces turnaround time in report writing, brings standardization, and improves accuracy.
Chatbots & Virtual Assistants
The chatbots that generate extremely context-specific responses are the most effective ones. Popular virtual assistants like Alexa, Cortana, Siri, and Google Assistant use AI technologies to understand the queries we raise, process the data, and present us with the desired results. Natural Language Generation, along with Natural Language Processing helps businesses to automate the customer service processes by generating personalized and accurate responses to the customers’ queries and complaints.
Apart from these, Natural Language Generation has applications in the areas of Risk and Compliance Management, Predictive Maintenance, Fraud Detection, and Anti-Money Laundering, Customer Experience Management, Automated Journalism, and many more.
Coming to business, you would 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 critical data points resulting in more insightful information to sell and pass across; thereby working towards an increase in your revenue. With NLG, you can communicate good insights at a 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, while leaving reporting and data analytics to NLG. To begin your NLG journey, get in touch with us.
Romil Shah is an AI enthusiast with considerable experience across varied technology domains, primarily Natural Language Generation (NLG), and Blockchain technology. He is passionate about technological innovation and more importantly its real-world applications. His work within the field has brought about constructive conversations and explorations around AI and its extended use in businesses.