What if there was a way for computers to analyze and convert raw data into simple, human language? Fortunately, this is already being done today via Natural Language Generation (NLG).
While we human beings continue to adapt and evolve our skills in various scientific fields, it’s our ability to understand and communicate with each other that supersedes them all. Businesses worldwide are expanding their services based on thoughts and ideas being exchanged every day.
NLG has been a part of AI research since 1986, but crucial advancements in this technology have only taken place in the last few years. Today, whether we know it or not, we use NLG in performing everyday tasks.
Let’s take a deeper look into what is Natural Language Generation and how it works.
What is Natural Language Generation?
Natural Language Generation (NLG) is a subcategory of Natural Language Processing (NLP), the computational study of human language to discover data patterns and perform automated tasks. NLG uses Artificial Intelligence (AI) software to produce simple human language based on datasets.
Just like an analyst, it studies big datasets and converts it into natural-sounding language at an extraordinary scale, accuracy, and pace. When given the correct format, it can turn numbers and statistics into data-driven narratives.
When machines take over this monotonous everyday task, humans can focus on other innovative and developmental activities.
Today, NLG generates reports in various industries like journalism, marketing, financial services organizations, etc.
For example, producing quick sound-bites for live news can be daunting without AI. Another example is the predictive text option in our smartphones that studies our writing style and helps complete our sentences in real-time. NLG is also used to create blog posts, suggest titles, summarize reports, chatbot responses, etc.
What is the Correlation Between NLP, NLU, and NLG
Natural Language Processing (NLP) is often considered the parent technology of Natural Language Understanding (NLU) and Natural Language Generation (NLG).
Let us understand the correlation between the three
- Natural Language Processing is the field that processes and analyzes large data sets of human language to understand and track patterns. It has various steps involved in reading text, breaking it down, understanding it, and predicting responses. For example, chatbots work using NLP. They read the text sent by the user, read the algorithm and data, find suitable keywords and then respond — all within seconds.
- Natural Language Understanding is a subcategory of NLP dedicated to interpreting the meaning of large data sets of human language fed into the software. So, NLU is a predecessor of NLP. It involves syntax, semantic, and pragmatic levels to understand unstructured human data.
- Natural Language Generation is the process of producing natural-sounding human language — after performing NLU and NLP.
What is the Difference Between NLP, NLU, and NLG
- NLP is used to analyze and process large datasets of human language.
- NLU is used to understand and extract meaning from human language.
- NLG is used to convert data into natural-sounding human language.
While NLG can write text, it can’t read. Similarly, while NLU can read text, it can’t write. So, NLU and NLG are sub-sets of NLP.
Therefore, all three processes go together to perform the tasks of analyzing, converting, and producing natural-sounding human text.
Types of Natural Language Generation
There are primarily three types of NLG:
Basic NLG
This is the simplest form of NLG. It gathers a few simple data points based on commands and converts them into a written script as per a pre-defined format. For example, the weather report can use basic NLG to predict everyday conditions. “The humidity is 30% today” is a simple example. However, this approach is quite limited when compared to others.
Template-Driven NLG
At the next level, we have template-driven NLG. This process uses templatized paragraphs to generate text based on given data sets. It uses a set of rules, placeholders, commands, and conditions to analyze and produce written text. For example, stock market reports, sports scores, financial reports, etc., can be created with this approach. Although template-driven NLG is more powerful than basic NLG, it still lacks the ability to produce linguistically complex text.
Advanced NLG
Advanced NLG offers better flexibility than the other two. It can produce distinct, elaborate, conclusive, and well-structured human language. It uses morphological, lexical, and grammatical patterns to generate actionable insights in its output text. For example, detailed annual business reports use advanced NLG to generate deeper insights from their business data.
Ways To Perform Natural Language Generation
There are six stages involved in performing NLG, each stage refining the dataset required to be converted into human text:
Content Analysis
The first step involves identifying the primary topics in the data source and their correlation. This is used to determine what content needs to be produced at the final stage of NLG.
Data Understanding
As the name suggests, this step is used to analyze and understand the data and statistics involved in the process. It uses machine learning to track patterns and contextualize the data.
Document Structuring
The most suitable document plan and structure are pinned down to produce the final text. This also includes structuring the narrative, similar to how a story is shaped.
Sentence Aggregation
In this step, phrases are aggregated into sentences based on their relevance in the text. This is used to ensure that the topic is summarized elaborately and accurately.
Grammatical Structuring
This is a crucial step, as it is related to applying grammatical rules and ensuring that the text being produced is semantically correct. If any errors are found, this process rewrites them to ensure language accuracy.
Language Presentation
This is the final stage of NLG, where the natural-sounding text is put into a proper template and format ready for use.
Tools That Offer NLG Services
There are thousands of tools in the market that offer NLG services for various industries and business purposes. Following are some of the most sought-after NLG tools that can help you automate everyday marketing tasks:
- Arria: Arria is a form of AI that transforms structured data into a natural-sounding language. They offer services for enterprise reporting, financial services, life sciences, oil and gas, restaurants, consumer research, sports, consumer goods, retail, and real estate, among others.
- AX Semantics: Ax Semantics AI-powered software offers easy-to-use content writing services in over 110 languages with 80% time-saving. You can use it to generate content for e-commerce websites and travel websites, among others.
- Automated Insights: Automated Insights helps organizations enhance their business intelligence dashboards and performance reports with targeted content.
- Amazon Polly: Amazon Polly’s text-to-speech service turns text into lifelike speech, allowing you to create apps that can talk and build speech-enabled products.
- Clickvoyant: Clickvoyant is a single, easy-to-use tool that helps eCommerce and digital advertising firms quickly deliver revenue-generating marketing insights in the form of simple presentations.
- Quill: Quill helps you automatically create data-driven stories in seconds by uploading your data directly from your BI tool like Tableau, Power BI, Qlik, or your in-house data visualization tool.
- Drift: Drift is a “revenue acceleration platform” that helps your marketing and sales teams deliver personalized customer experiences in real-time via live chat, virtual selling assistants, email, video, and other automation products.
Main Components of Natural Language Generation
Here are the five major components of NLG:
Aggregation
This is the process of combining or “aggregating” two or more sentences or paragraphs using various conjunctions. For example, “Liam likes hamburgers.” and “Liam likes fries.” can be combined into “Liam likes hamburgers and fries.”
Lexicalization
This is the process of determining and choosing a word based on its context rather than simple grammatical construction. For example, between “took off” and “left,” “the flight took off” is better suited than “the flight left.”
Referring Expression Generation
This technique determines which words or phrases to use based on the previously given context. This process could be used in determining pronouns. For example, “Miss Gitler wasn’t feeling well. She went to see the doctor.”
Using discourse markers: This technique involves using discourse markers like “also,” “oh,” “well,” “then,” “I mean,” “but” to make the sentence flow smoother. For example, “Sonia thought it was the right thing to do, but her sister did not agree.”
Linguistic Realization
Using this technique, NLG derives the understanding of creating human language in a given context. For example, consider the sentence, “Sally took the morning train from Dublin to Galway.” Here, with linguistic realization, the subject’s name and the city names have been capitalized, and “from” and “to” have been added as well.
Benefits of Using Natural Language Generation
There are numerous benefits of using NLG and its deep learning to create content in natural human language. You can leverage the following benefits by using NLP for content creation:
Generate High-Quality Content
NLG can help you generate high-quality content, detect grammatical errors, and correct them. The more training the software undergoes, the better text it generates. Using NLG, your content teams can save time in monotonous tasks and invest more time in innovation and creativity.
Save Up Human Effort for Driving innovation:
Some people might wonder where human content creators fit into the equation once NLG takes over. The answer is simple: NLG helps content creators work out mundane and repetitive tasks. They can edit and add their insights into the content, getting it quickly ready for publishing! That’s a win-win for all.
Increase Content Generation:
Using this technology can drastically help improve the amount of quality content being put out by organizations. Marketers can publish quality content with SEO, improve their search ranking and web traffic, and generate better leads quickly. This is not to say that NLG can replace human creative writing, but it can help sketch up a rough draft to get you started.
Personalized Experiences
NLG can help personalize digital experiences for hundreds of customers in real-time, which may not be manually possible for many organizations. For example, using chatbots for live chat can help deliver personalized customer experiences, ultimately helping generate revenue.
Creating Topical Coverage
It might not always be feasible for small organizations to develop content on niche topics for multiple audience segments. Here, NLG can help bring down the cost and make it profitable. Brands can engage niche audiences that weren’t interested before and make more sales.
Examples of Using Natural Language Generation
As previously mentioned, NLG has numerous benefits for various industries, including financial and legal firms, pharma and healthcare, entertainment, retail, eCommerce, manufacturing, and logistics, among others.
Here are a few examples of using NLG applications:
Chatbots and Virtual Assistants
Chatbots are some of the most common use cases of NLG. Virtual assistants like Alexa, Siri, and Google Assistant use NLP to analyze our queries and generate meaningful responses. NLG also helps organizations design context-specific customer services with personalized experiences. This technology can help detect security risks, fraud, spam cases, compliance management, etc. On an enterprise level, NLG helps deliver text and collect precise data points that can help generate business-critical insights and increase revenue opportunities.
Analytics and Reporting
Generating deeply insightful reports might be the second most-used case of NLG. Organizations all over the world today are using NLG to read large data charts and complex graphs and convert them into meaningful, straightforward reports. This helps them draw insightful conclusions, summarize their reports and save business-critical time.
Automating Content Requirements
NLG can help create quality content faster, automating monotonous and tedious everyday tasks. It can help build basic reports with statistics, which content teams can edit and refine, thereby saving time and resources.
Natural Language Generation Use Cases in Various Industries
Let’s take a quick look at how financial services, insurance, and life sciences organizations use NLG for growing their business.
Financial Services Companies
Financial services firms use NLG to create detailed portfolio reports for their user accounts. Like a dedicated business analyst, NLG assists portfolio managers in developing detailed reports, updates, and market trends to share with their customers and help in the decision-making process. They can also help quickly turn data sets into fact sheets, written summaries, and graphics in various languages — helping them reach a larger geographical customer base.
Gramener’s Smart Contract Risk Identifier (SCRI)
The Smart Contract Risk Identifier is a legal document analyzer powered by artificial intelligence that supports lawyers in conducting precise document reviews. Legal documents can be fairly complicated, with hundreds of different categories and clauses to consider. Manually evaluating a Non–Disclosure Agreement (NDA), for example, could take many hours.
Our solution employs clauses to automatically categorize a document. It allows lawyers to quickly assess the significance of a clause and take action on the most important. With NLG, lawyers can easily analyze legal documents.
This tool saves a lot of time for professionals like attorneys, allowing them to focus on more productive tasks.
Insurance Firms
As financial services companies, NLG helps insurance firms create claim and performance reports for their customers. But that’s not all! Financial firms can also use NLG to assist call center teams with real-time customer feedback and surveys. This can help improve their services and customer satisfaction ratings, ultimately helping to grow their revenue.
Healthcare and Life Sciences Organizations
For the healthcare and life sciences sector, the benefits of NLG are only just being realized. Like insurance firms, NLG helps the healthcare sector improve its customer service. They can summarize large amounts of medical research data and analyze it without human bias.
Conclusion
There’s no doubt that NLG has become the go-to technology for most enterprises to help automate operations, but we’re only beginning to scratch its surface. Today, it helps comb through large data sets and summarizes every day and straightforward language. In addition to the number of man-hours saved every day!
Artificial Intelligence is only getting faster, quicker, and better. It will play an essential role in the future as we automate mundane everyday tasks and spend most of our time on strategic, innovative, and creative projects. It is crucial to harness its power to benefit the human race today and for future generations.
As NLG techniques evolve, they will help us better understand and predict our health risks, businesses, job market, and changing environmental conditions. But it will not just be presented to us in pie charts, graphs, and statistical reports. Hopefully, the future of NLG interaction will be more natural, humane, and sentient to our needs.
This is not to say that human interactions will get lost in translation. Nothing can replace human imagination and creativity. However, technologies like NLG can accelerate the way we do business and help make critical decisions based on facts. The dual combination and balance between human creativity and AI can help solve real-world problems and create a better place for future generations.
