Foundations of Natural Language Understanding (NLU)

• Basic knowledge of programming (Python)
• Interest in AI and language technologies

Audience

• AI beginners
• Data science students
• Tech enthusiasts

Natural Language Understanding (NLU) is a key subfield of Natural Language Processing (NLP) that focuses on teaching machines to understand and interpret human language. This course introduces participants to the basic principles of NLU, key algorithms, and practical applications, including the distinction between NLP and NLU.

This instructor-led, live training (online or onsite) is aimed at beginner-level participants who wish to learn the fundamentals of NLU and how it fits into the larger NLP landscape.

By the end of this training, participants will be able to:

• Understand the foundational concepts of Natural Language Understanding (NLU).
• Differentiate between NLU and NLP.
• Explore basic techniques for language interpretation and context understanding.
• Apply NLU techniques to simple text processing tasks.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Natural Language Processing (NLP)

• Overview of NLP and its applications
• Key components: syntax, semantics, and pragmatics
• The role of NLU within NLP

Understanding NLU Concepts

• Definition and scope of Natural Language Understanding
• Differences between NLU and NLP
• Basic algorithms used in NLU

Basic NLU Techniques

• Tokenization and sentence segmentation
• Named entity recognition (NER)
• Sentiment analysis and text classification

Language Modeling in NLU

• Introduction to statistical and neural language models
• Exploring word embeddings and context-aware models
• Applications of language models in NLU tasks

Challenges in NLU

• Ambiguity in natural language
• Contextual understanding and disambiguation
• Dealing with low-resource languages

Applications of NLU

• NLU in chatbots and virtual assistants
• Information extraction from unstructured text
• Case studies in various industries

Future Trends in NLU

• Advancements in deep learning for NLU
• Emerging techniques in contextual understanding
• The future of human-AI communication

Summary and Next Steps