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Natural Language Generation (NLG)

Updated: Aug 1

Natural Language Generation (NLG) is a growing field of study within the realm of Artificial Intelligence (AI), focusing on creating human-like language output based on structured data or other inputs. NLG technology has seen considerable advancement in recent years and has become increasingly popular across various industries and applications.

NLG is applied in a broad range of sectors, including finance, marketing, healthcare, and e-commerce, among others. The following are some of the most prevalent NLG applications in use today:

  1. Automated Reporting: NLG can be utilized to generate reports from massive data sets. Automated reporting can save time while also eliminating human errors, ensuring the report's accuracy and consistency.

  2. Customer Service: NLG can provide customer service through chatbots and virtual assistants. These applications can guide customers through the purchasing process and answer frequently asked questions.

  3. Content Creation: NLG can generate content such as news articles, product descriptions, and social media posts. These applications can create high-quality content quickly and at scale.

  4. Business Intelligence: NLG can analyze data and provide insights. These applications can help businesses make data-driven decisions by identifying patterns and trends in massive data sets.

NLG offers numerous benefits, including improved efficiency, accuracy, scalability, and cost-effectiveness. NLG can automate time-consuming tasks, reduce human errors, produce output quickly and at scale, and save businesses money by reducing the need for manual labor. In conclusion, NLG is a powerful technology that can automate tasks, improve accuracy, and provide insights at scale. NLG applications are widely used across various industries, and as NLG technology continues to advance, we can expect even more exciting applications in the future. References:

  1. Sallie Mae: How natural language generation is transforming the student loan industry. (2022).

  2. IBM: Natural language generation. (2021).


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