Explained: Natural Language Generation (NLG)

Natural Language Generation – explore this AI subfield, turning data into understandable text for chatbots, reports, personalized marketing, and more.

What Is Natural Language Generation (NLG)?

Natural Language Generation (NLG) is essentially the art of getting computers to speak and write like humans. It’s a subfield of artificial intelligence (AI) and computational linguistics that focuses on developing software processes to produce understandable and coherent text in response to data or information.

What Is The Difference Between Natural Language Generation & Natural Language Processing?

Both natural language generation (NLG) and natural language processing (NLP) deal with how computers interact with human language, but they approach it from opposite ends.

For instance, NLP focuses on understanding human language. It converts unstructured data like text or speech into a format computers can understand. This involves tasks like:

• Named Entity Recognition: Identifying important entities like people, places, and organizations.
• Part-Of-Speech Tagging: Classifying words according to their grammatical function (noun, verb, etc.).
• Sentiment Analysis: Understanding the emotional tone of a text.
• Machine Translation: Converting text from one language to another.

On the other hand, natural language generation (NLG) focuses on creating human language. It converts structured data into natural-sounding text or speech that humans can easily understand. This involves tasks like:

• Text Summarization: Generating a concise summary of a longer text.
• Question Answering: Providing answers to questions based on a given text.
• Report Generation: Creating reports from structured data like financial statements or scientific results.
• Dialogue Generation: Creating chatbots that can hold conversations with humans.

Also Read: Explained: Markov Chain Monte Carlo

NLP and NLG often work together, as NLP can be used to prepare data for NLG, and NLG can use the results of NLP to generate more accurate and relevant text. Here are some more differences between the two technologies:

• Focus: NLP focuses on meaning, while NLG focuses on form and fluency.
• Input & Output: NLP takes human language as input and outputs structured data, while NLG takes structured data as input and outputs human language.
• Applications: NLP can be used for machine translation and sentiment analysis, while NLG can be used for chatbots and report generation.

How Does Natural Language Generation (NLG) Work With AI?

Natural language generation (NLG) and AI are deeply intertwined, with NLG relying heavily on various AI techniques to achieve its goals. The following are some of the AI techniques used in NLG:

• Machine Learning is the backbone of NLG, allowing systems to learn from massive amounts of text data and identify patterns. NLG models use machine learning to understand language structure, grammar, and vocabulary and then use this knowledge to generate new text.
• Deep Learning: A specific type of machine learning, deep learning utilizes artificial neural networks with multiple layers to process language more accurately and capture complex relationships within the text. This enables NLG systems to generate more nuanced and human-like text.
• Natural Language Understanding (NLU): This AI subfield focuses on understanding the meaning of human language. NLG systems often use NLU techniques to analyze a situation’s context and generate relevant and appropriate text.
• Knowledge Representation: This involves encoding information in a way computers can understand. NLG systems use knowledge representation to access and process relevant information from various sources, enriching the generated text with factual details.

AI also enables many of the capabilities NLG possesses:

• Personalization: AI allows NLG systems to tailor their output to specific users or situations. This can be done by considering user preferences, demographics and context.
• Creativity: While NLG isn’t truly ‘creative’ in the human sense, AI helps it generate different phrasings, sentence structures and even narratives depending on the situation.
• Fluency & Coherence: AI helps NLG systems produce grammatically correct text that reads naturally and flows logically.
• Adaptability: AI allows NLG systems to learn and improve over time, adapting to new data and situations.

Also Read: Explained: Overfitting

What Are Some Common Examples Of Natural Language Generation (NLG)?

The following are some of the several real-life applications of natural language generation (NLG):

• Chatbots: AI-powered NLG allows chatbots to have more engaging and natural conversations with users, understand their intent, and respond in a relevant and personalized way.
• News & Report Generation: NLG can automatically generate news articles or reports based on data, saving journalists and analysts time and resources.
• Personalized Marketing: AI-powered NLG can create personalized marketing messages that resonate with individual customers, improving engagement and conversion rates.
• Accessibility Tools: NLG can convert complex information into simpler formats, making it accessible to people with disabilities.