How Natural language generation is Connected with AI?
Artificial Intelligence is troublemaking industries with various use cases. Natural language generation (NLG) is the AI technology behind text content automation with its capability to convert data into words, sentences, articles and even film scripts.
Here, we discuss all important aspects of NLG, including why it matters, and how it works and use cases.
Natural Language Generation
Natural Language Generation (NLG), a subcategory of Natural Language Program (NLP), is a software process that automatically transforms structured data into human-readable text.
Importance of Natural Language Generation
Most of the customers read blogs and websites before deciding which products to buy. For many e-commerce and retail firms, it is difficult to generate content for each product manually. NLG technology can automate this process. Thus, improve overall marketing/sales efforts of the companies.
NLG market also has potential because:
Available data is increasing and text is easier to digest than data and can help communicate data more effectively.
In the age of digitalization & AI, consumers expect personalization and NLG can provide it at scale.
Working of NLG
An automated text generation process involves 6 stages.
Content Determination
The limits of the content should be determined. The data often contains more information than necessary.
Data interpretation
The analyzed data is interpreted. Thankful to machine learning techniques, patterns can be recognized in the processed data. This is where data is put into context.
Document planning
In this stage, the structures in the data are organized with the goal of creating a narrative structure and document plan.
Sentence Aggregation
It is also called micro planning, and this process is about choosing the expressions and words in each sentence for the end-user.
Grammaticalization
Grammaticalization stage makes sure that the whole report follows the correct grammatical form, spelling, and punctuation. This includes validation of actual text according to the rules of syntax, morphology, and orthography.
Language Implementation
This stage involves inputting data into templates and ensuring that the document is output in the right format and according to the preferences of the user.
Use cases of Natural Language Generation
Since NLG aims to make sense of the data and create human-readable insights, it can be applied to all areas dealing with reporting, content creation, and content personalization.
Retail & Wholesale
NLG solutions can provide product descriptions and categorization for online shopping and e-commerce and help personalize customer communication via chatbots.
Banking & Finance
The banking industry highly relies on data and insights for performance reporting. Additionally, profit and loss reports can be automated using NLG systems. NLG techniques can be used to support fintech chatbots that interact with customers for personal financial management advice.
Manufacturing
As IoT applications are implemented more widely in production sites, they generate a significant volume of data useful for performance improvement and maintenance. NLG can automate the communication of important findings such as IoT device status and maintenance reporting so employees can take action faster.
Media
NLG solutions can aid summarization and content creation. Especially sports and financial news (also called robot journalists) tend to follow similar templates, and text explaining such events can be easily created.
Insurance
NLG solutions can help to improve the communication of personalized plans for customers.
Transportation
Chatbots can deliver alerts about delays and schedules. NLG tools can be used to create personalized, easy to read travel plans.
Politics
Probably the most dangerous use case is using NLG solutions to spread personalized propaganda and misinformation. Unfortunately, this is the risk of making the current flow of political disinformation even more dangerous and personalized.
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