Natural Language Generation
Natural language generation is a subtype of artificial intelligence that takes data and converts it into natural-sounding language as if it were written or spoken by a human.
While this capability isn't new, it has advanced significantly in recent years, and there has been a considerable increase in enterprise-wide usage of NLG to improve operational efficiency, human productivity, and even customer engagement.
A machine can process a large quantity of data with high precision, and the purpose of NLG systems is to figure out how to best communicate the data's conclusions or analysis.
Humans may focus on more complicated communication initiatives when organizations use technology to automate ordinary communication duties.
We are living in the era of advanced NLG, where machines can communicate with humans in the same way that humans can. NLG algorithms analyze what would be interesting or important to transmit to a certain audience, then translate that intelligent insight into content that is full of audience-relevant information and written in conversational language. Learn more about NLG through the link.
Working of NLG:
NLG is a multi-stage process, with each level refining the data utilized to generate content using natural-sounding language. The following are the six stages of NLG:
Examine the content:
Data is filtered to identify what should be included in the final content. Identifying the key subjects in the source document, as well as the relationships between them, is part of this stage.
The data is analyzed, patterns are discovered, and the information is placed in context. At this point, machine learning is frequently applied.
Based on the sort of data being analyzed, a documented plan is constructed and a narrative framework is chosen.
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It is a technique for combining sentences. Sentences or sections of sentences that are relevant to the issue are mixed in ways that accurately summarise the topic.
To generate natural-sounding writing, grammatical rules are employed. The sentence's syntactical structure is deduced by the software. It then rewrites the statement in a grammatically accurate manner using this information.
Presentation of the language:
The final output is generated using a template or format chosen by the user or programmer.
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6 steps to natural language generation (source)
Variants of NLG
For applications that demand a simple translation of data into text, templated NLG systems are ideal.
However, intelligent NLG systems that conduct more than rules-based operations focused on fitting data into pre-existing templates are required for those wishing to deliver data-driven information in a scalable manner.
NLG tools for the enterprise go beyond simply reporting facts in data. They can express the most fascinating and relevant concepts found in data in understandable language, such as:
By comprehending the context of what needs to be communicated, identifies and articulates the most important findings.
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Using natural, conversational language to communicate complicated concepts simply and understandably.
Scaling data-driven communications that update whenever the underlying data changes on time.
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Types of NLG:
Natural Language Generation (NLG) in AI can be divided into three categories based on its scope: Basic NLG, Template-driven NLG, and Advanced NLG.
At the most basic level or basic NLG, a few data points would be identified and gathered, then transcribed into sentences. For example, consider the following weather report: "the humidity today is 78 percent."
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The next level of NLG, also known as template-driven NLG, uses template-heavy paragraphs to generate language based on dynamic data, as the name suggests. Hard-coded rules, canned text, placeholders, and custom data representations are all used.
Preliminary business rules are used to develop language, which is driven by looping commands such as if/else statements. This type of NLG can be used to create sports score charts, stock market updates, and simple business reports.
Basic and template-driven Natural Language Generation technologies are less versatile than advanced NLG solutions.
It employs Machine Learning to transform data into narratives with a distinct beginning, middle, and endpoints.
This type of NLG is carried out using deep learning neural networks that learn lexical, morphological, and grammar patterns from written language.
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Applications of Natural Language Generation:
Finance, Human Resources, Legal, Marketing, Sales, Operations, Strategy, and Supply Chain can all benefit from Natural Language Generation.
Financial services, pharmaceuticals and healthcare, media and entertainment, retail, manufacturing, and logistics are among industries that can benefit greatly from this technology.
Following are some of the most common NLG examples and applications:
NLG is used by businesses in a variety of industries to generate reports. Data may be analyzed to obtain actionable insights and then converted into accessible reports using NLG-powered Business Intelligence solutions.
Natural Language Reporting can turn a slew of data charts and sophisticated graphs into clear, natural-language insights. These insights can help business leaders quickly reach conclusions and make good decisions, saving them a lot of time.
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By stringing long sequences of phrases together and creating personalized content, NLG can be utilized to automate content generation.
Internal communications, product descriptions, agreements, company reports, contracts, and other types of textual communication can all benefit from this technology.
Report writing turnaround time is reduced, standardization is achieved, and accuracy is improved by automating manual writing.
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Virtual Assistants & Chatbots
The most effective chatbots are those that generate exceptionally context-specific responses.
AI technologies are used by popular virtual assistants such as Alexa, Cortana, Siri, and Google Assistant to comprehend our questions, process the data, and present us with the required outcomes.
Natural Language Generation, in conjunction with Natural Language Processing, assists organizations in automating customer care procedures by providing personalized and correct responses to client inquiries and complaints.
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Natural Language Generation also has applications in Risk and Compliance Management, Predictive Maintenance, Fraud Detection, and Anti-Money Laundering, Customer Experience Management, Automated Journalism, and many more fields.
When it comes to business, you could be wondering how it might benefit a company.
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You can assemble more big data with Natural Language Generation, and by assembling more big data, you can acquire even more crucial data points, resulting in more insightful information to sell and pass along, increasing revenue.
When compared to manual efforts, NLG allows you to share good findings at a faster and broader scale, enhancing the organization's total analytic output.
For performance reporting, the banking industry heavily relies on data and insights. NLG systems can also be used to automate profit and loss reports.
Fintech chatbots that communicate with customers for personal financial management advice can benefit from NLG approaches.
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As IoT applications become more frequently used in commercial environments, they create a large amount of data that may be used to improve and maintain performance.
NLG can automate the communication of critical data like IoT device status and maintenance reporting, allowing employees to act more quickly.
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NLG can considerably minimize time-consuming and intensive data processing, as well as manual reporting, leading to improved operational proficiency.
Furthermore, NLG would allow you to provide all clients with tailored, up-to-date, data-driven, and straightforward information based on their demands.
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Natural Language Generation is one such instrument that equips us with utilizing this huge data while not allowing our creative energies to dry out in the humdrum data translation operations. Leave reporting and data analytics to NLG, and save your brains for decision-making and action planning.