Natural Language Generation, or NLG, is a part of Artificial Intelligence that generates easy-to-understand language for humans. It is a part of Natural Language Processing, which includes Natural Language Understanding and Natural Language Generation.
This process transforms structured data into understandable language, which is one of the reasons why NLG is vital in communicating complex insights in a simple, understandable way.
What Are the Applications of NLG?
NLG is widely used to:
• Generate automated reports
Breaking down large and complex data using NLG is an application often used in business analytics. NLG generates detailed and easy-to-understand summaries from raw data such as market trends, operational metrics, and sales figures.
• Enable virtual assistant and chatbot communication
NLG plays a vital role in enabling virtual assistants and chatbots to generate relevant responses in a human-like manner when interacting with users. The NLG technology uses the available data and conversation content to generate specific answers to user queries. By doing so, it generates dynamic responses to ensure they are relevant to specific user interactions.
• Generate multi-formatted content
NLG automates content creation in various formats, such as social media posts, product descriptions, data-based news articles, etc. This technology customizes content for target audiences, ensuring the tone, style, and details are accurate and appropriate.
• Create tutoring materials
NLG creates interactive educational materials like quizzes, lessons, and language exercises customized to learners’ proficiency and progress. Learners can also benefit from NLG’s real-time feedback to understand language nuances and improve their linguistic skills.
• Enhance customer service
NLG’s personalized responses via customer emails, voice interactions, and chat messages significantly improve customer service. Automated systems or customer service staff can deliver more relevant responses to customer queries since they are based on previous interactions and customer data.
FAQs
1. How is NLG different from NLU and NLP?
Natural Language Generation (NLG) is the process of generating written or spoken language from data. On the other hand, Natural Language Processing is a comprehensive field encompassing Natural Language Understanding (NLU) and Natural Language Generation of human language by machines.
2. What are the commonly used NLG algorithms?
Some commonly used algorithms are:
- Recurrent Neural Networks (RNN)
- Long Short-Term Memory (LSTM)
- Markov Chains
- Transformers
Each algorithm has a unique mechanism for language generation.
3. Is it possible to create content in multiple languages using NLP?
Yes, advanced NLP systems can produce multi-lingual content while adapting to many language and context nuances.
4. How does Machine Learning aid NLG?
Machine Learning supports NLG by allowing models to learn from data and use patterns and relationships to generate text.
5. Does NLG facilitate voice assistant features?
NLG improves the interaction quality of voice assistants by generating natural-sounding answers to users’ questions.
5. What are the steps in NLG?
Natural Language Generation comprises six steps:
1. Data analysis – Collating primary topics and relationships from structured and unstructured data.
2. Data comprehension – Using NLP and Machine Learning for data pattern and context interpretation.
3. Document creation – Creating document plans based on data-driven narratives.
4. Sentence aggregation – Combining connected sentences to create abstract information.
5. Grammar Structuring – Applying NLP grammar rules to generated text.
6. Language presentation – Generating the output in the format chosen by the user.
6. What are the uses of NLG?
Natural Language Generation (NLG) is used to:
- Transform structured data into text readable by humans
- Automate the generation of text content for summaries, reports, articles, and personalized messages.
- Communicate NLP-based information for content creation, data analytics, and business intelligence.
Related Terms
