Almost every company today is attempting to embrace AI and capitalise on its potential by implementing an intelligence-driven system that takes, processes, and synthesises data, resulting in automated data analysis and content management. Despite Big Data's enormous success and adoption, research suggests that only 20% of employees with access to business intelligence tools have the literacy or domain expertise to use them effectively. Data presented using charts and graphs, on the other hand, does not appear eye-friendly, which can lead to misinterpretation and poor decision-making. This is where the Natural Language Processing, Natural Language Understanding, and Natural Language Generation subsets of AI technologies, as well as their analytical algorithms, come into play.
Businesses previously required a certain level of human and continual monitoring to ensure that semi-smart robots understood and followed a pre-programmed algorithm. Machines became clever enough to address specific business objectives and goals as Artificial Intelligence, machine learning, artificial neural networks, deep learning, natural language processing, and natural language generation advanced. These AI-based technologies, when streamlined and properly harnessed, can comprehend large datasets and offer useful insights, which can then be used to develop personalised and impactful solutions. IT giants like Google, Apple, Microsoft and Amazon rely on such algorithms for better product suggestions, online search, voice-enabled mobile services, etc.
NLP, NLG, and NLU are supposedly sophisticated acronyms used to convey simple operations, despite their appearance as intimidating technical jargon. Here's how it works:
• NLP refers to the process by which computers read and convert input text into structured data.
• NLU refers to the comprehension of textual/statistical data recorded by computers.
• Natural language generation (NLG) is the process by which computers convert structured data into text and write information in human language.
The reading element of Natural Language Processing is complex and contains a number of functions, including:
• Language filters for indecent expressions
• Sentiment analysis for human emotions
• Subject matter classification
• Location detection
Natural Language Understanding is a subset of Artificial Intelligence that follows after Natural Language Processing in order to truly understand what the text suggests and extract the underlying meaning. NLU and NLG are used by conversational AI bots like Alexa, Siri, and Google Assistant to fulfil their goals.
Data has always been required for humans to generate and share new ideas. However, with a large influx of data to examine as well as the requirement to dramatically reduce expenses, businesses must find ways to streamline.
When it comes to Natural Language Generation, the main benefit is its capacity to turn large datasets into human-readable narratives. Unlike Natural Language Analyzing, which only examines texts to create insights, NLG may produce data-rich information by processing statistical data in spreadsheets.
Data may be reviewed, analysed, and communicated with precision, scalability, and accuracy using Natural Language Generation. Productivity rises as routine analysis and related processes are automated, allowing humans to focus on more creative, high-value-high-return activities.
The Associated Press used Natural Language Generation's report-generating capacity to create reports from corporate earnings data in an unusual use case. This eliminates the need for human reporters to spend time and effort sifting through large amounts of data before composing a report. Instead, because NLG can automate the creation of hundreds of narratives once they've been set up precisely, they can devote their efforts to other important activities.
Natural Language Generation has benefits that go beyond what most people think of when it comes to AI adoption. The following are some of its marketing and company management advantages:
The ability of NLG to produce an orderly structure of data from information processed in prior stages of NLP and NLU is its primary capability. NLG can automate the production and deliver documentable forms of data such as analytics reports, product descriptions, data-centric blog entries, and more by placing this well-structured data in a carefully prepared template. In this situation, algorithmically programmed computers are free to create material in any format that content developers wish. The only thing left for them to do now is to advertise it through major media channels to the intended demographic. For content creators and marketers, Natural Language Generation serves two purposes:
1. Content generation automation
2. Data delivery in the expected format
Web mining is used in content generation, and search engine APIs are used to create effective content from diverse online search results and references.
Several NLG-based text report generating systems have been developed to date to generate textual weather forecast reports from input weather data.
Additionally, a company tasked with producing accurate weather forecast reports will be able to use Natural Language Generation's real-time analytical power to convert the statistical structure of weather forecast data into an ordered, reader-friendly textual format.
It is no longer necessary to hire and train data-literate professionals once Natural Language Generation is in place. As far as corporate theories go, human force is essential for comprehending consumer interests, demands, and transforming them into written narrative.
Machines are designed to evaluate what customers desire, find crucial business-relevant insights, and prepare summaries around it via Natural Language Generation.
When you realise how expensive and ineffective it is to hire individuals who spend hours trying to grasp complex data, the value of NLG doubles. By 2018, Gartner expects that 20% of corporate content will be written by machines utilising Natural Language Generation and integrated into major smart data discovery systems. Humans will no longer be required to prepare legal documents, shareholder reports, news announcements, or case studies.
Given that certain products have very high margins, inventory management success for any store results in a significant increase in terms of business goals and total resultant profit. Data is everything, and it's crucial in areas like supply chain, manufacturing rate, and sales analytics. Store managers can use this data to make judgments about how to keep inventory at optimal levels. However, expecting managers to be data-savvy and understand data effectively is not always realistic.
When it comes to advanced NLG, it can be used as an interactive medium for data analysis, resulting in a smooth and intelligent reporting process. Rather than having to go through multiple charts and bar graphs of data, shop managers receive clear narratives and analysis in the format they want, indicating whether or not they will need a specific item the following week. Managers have the greatest predictive model with clear information and recommendations on store performance and inventory management thanks to natural language creation.
In order to increase a call center's effectiveness, it's a good idea to conduct performance reviews and provide accurate training. Charts, on the other hand, won't help much in communicating the particular pain points and places for improvement unless they include strong narratives in the form of feedback, as discussed in the preceding use cases. This is where the benefits of using Natural Language Generation in conjunction with NLP may be found.
NLG can be strategically incorporated into main call centre processes, generating individualised training reports based on in-depth analysis of call records and performance actions. It may clearly show how call centre staff are performing, their progress, and where they need to improve in order to meet a specific goal.
Chatbots will be significantly more intelligent if they are configured correctly, and will no longer give only plain chats for queries and resolutions, but will also engage, explain, and illuminate using advanced NLG. Advanced Natural Language Generation, when combined with enterprise-specific workflow management, will help to establish a far superior network of engagement among managers, executives, employees, and consumers, empowering company dynamics and producing correct output in a short amount of time. Finally, the advantages of Natural Language Generation can be used to automate the compilation of reports, content generation, and the extraction of actionable insights for enterprises facing data analysis and multilanguage support difficulties. With NLG in place, struggling businesses can move beyond conversational chatbots and implement an automated, goal-oriented system for efficiently delivering information in the way that the end user expects. Enterprises that want to deploy Natural Language Generation-based conversational interfaces, virtual assistants, or software applications must work with the proper technology suppliers and innovation partners who have experience delivering full AI-powered system solutions.
Posted By InnoTechzz