Have you ever talked to Siri or Alexa and received a clear answer? That's the magic of the Natural Language Generation. NLG is a part of artificial intelligence and helps machines talk to us in a way we can easily understand. It works fast and saves time by turning numbers, facts, or patterns into helpful information. NLG is used in many places today, such as translating languages, writing product descriptions, generating personalized emails, crafting detailed reports, and answering questions through a chatbot.

Undoubtedly, NLG saves time, lowers costs, and opens a window into the future of technology. If you are a business leader looking to streamline operations, a tech enthusiast, or a curious mind, understanding NLG will bring more opportunities to use AI in smarter and more effective communication. To understand NLG or how it works, you can keep reading!

What is the Natural Language Generation?

Natural Language Generation is a type of artificial intelligence that helps computers turn data into written or spoken words. It is connected to how humans and machines communicate. NLG works with other tools like NLP and NLU. NLG aims to create programs that explain data in a way people can easily understand. These programs can look through a lot of numbers. It can also find patterns and turn them into clear text. It is very helpful for writing news or quick updates. NLG works fast and can even create articles. You can even publish them without any editing. NLG helps turn raw data into easy-to-read stories or reports that make sense to everyone. There are two main types of natural language generation. Let’s discuss them below.

• Extractive NLG: This method takes exact words or sentences from the original text. It doesn’t change anything; it just picks and copies essential parts. This method is proper when the wording is the same, like legal or official documents. It is easier because it doesn’t create new text.
• Abstractive NLG: This type of NLG creates new sentences using ideas from the original content. It freshly rewrites the information, often using simpler or more precise words. This method is more advanced and requires more innovative systems like transformers. Abstractive NLG is best for creative tasks like writing articles more naturally.

How Does Natural Language Generation Work?

Natural Language Generation (NLG) involves several steps to turn data into human-like language. Here is how the process works:

1. Signal Analysis: First, the system looks at the input data to determine what it needs. This data can include typed words, voice recordings, and more. The system looks for patterns and relationships in the data to understand the topic. It also uses entity recognition to determine what is being talked about.
2. Data Interpretation: After finding the essential data, the system tries to understand it. It uses natural language tools to break down sentences, figure out the parts of speech, and understand the meaning. If the data is already prepared, it can skip this step.
3. Document Planning: Next, the system chooses what information to share and how to organize it. It plans the structure based on the type of data and what the user asked for.
4. Microplanning: In this step, the system plans how to write the information. It decides sentence length, word choice, and how to build paragraphs.
5. Surface Realization: Finally, the system writes out the content in natural language, using all the plans it made earlier.

NLG Models And Methodologies

Natural Language Generation uses different models and methods to turn data into human-like text. Here are the methods used by NLG early and now.

1. Templates: Templates use fixed sentence patterns with blanks to fill in the data. For example: “In [month], [year], our [location] store sold [amount] units of [item]." This is useful when the sentence structure doesn't change, like sales reports. However, templates don't work well for writing stories or recipes.
2. Rule-based Systems: These systems follow if-then rules. For example, if the temperature is below 32°F, the system might say below freezing. Programmers create these rules by learning from experts.
3. Statistical Machine Learning: This method finds patterns in large datasets. It uses tools like Markov chains to guess what words usually come next in a sentence. It’s more flexible than templates but needs a lot of data.
4. Deep Learning Models: These are more innovative systems like RNNs that understand word order and meaning. They are better at writing natural-sounding text and are used in tasks like translation.
5. Transformers: They are the most advanced models. They understand long texts better and create high-quality content. Tools like ChatGPT and Claude use transformers to talk like humans.

Natural Language Generation Use Cases

Natural Language Generation helps computers write or speak in a way that humans can understand easily. Here are some common uses of NLG:

1. Voice Assistants: Apps like Siri, Alexa, and Google Assistant use NLG to talk back to users. They understand your voice using special tools and reply with spoken answers.
2. Virtual Assistants: Many websites use chatbots or virtual helpers to answer customer questions. These bots use NLG to reply. If the question is difficult, they can pass it on to a human agent.
3. Language Translation: NLG helps translate text from one language to another. The system writes the translated version clearly, and humans can double-check or fix it if needed.
4. Data Summaries and Reports: NLG can turn difficult or large data sets into short, clear summaries. It helps business leaders read reports and make decisions quickly.
5. Content Writing: NLG can create product descriptions, marketing emails, or social media posts, saving businesses time.
6. Sentiment Analysis: NLG tools read reviews, social media posts, and other feedback to determine how people feel about a product or service. They then create summaries from that information.

Conclusion:

Natural Language Generation is changing the way we work with data and information. It allows computers to speak, write, and create content and naturally. Handling tasks that take people hours can save businesses a lot of time. With the help of different models, from templates to advanced transformers, NLG is getting smarter daily. It can now create applicable and even creative content that sounds human. NLG is becoming a part of our daily digital world. NLG helps turn complex data into straightforward, easy-to-read content for everyone.