CIQA: A Coding Inspired Question Answering Model - Session W1.4

Overview

Discover innovative approaches to question answering through a coding-inspired model that enhances natural language processing and information retrieval capabilities.

Syllabus

• Introduction to Question Answering (QA)
Overview of QA systems
Importance of QA in natural language processing (NLP)
• Fundamentals of the CIQA Model
Explanation of the coding-inspired approach
Key advantages of the CIQA model over traditional models
• Core Components of NLP in CIQA
Tokenization and text pre-processing
Named entity recognition (NER)
Part-of-speech (POS) tagging
• Information Retrieval Techniques
Overview of information retrieval in QA
Leveraging databases and search algorithms
• Deep Dive into the CIQA Model Architecture
Structure and flow of the CIQA model
Coding paradigms influencing the design
• Enhancing Information Retrieval Capabilities
Techniques for improving search accuracy
Contextual understanding and relevance scoring
• Implementing the CIQA Model
Step-by-step guide to setting up the model
Tools and frameworks commonly used
• Case Studies and Practical Applications
Real-world applications of CIQA
Analysis of successful implementations
• Hands-on Lab Sessions
Building a simple QA system using CIQA
Testing and evaluating model performance
• Future Trends in QA and NLP
Emerging technologies in question answering
Anticipated advancements in CIQA and similar models
• Course Recap and Q&A
Summary of key concepts
Open floor for participant questions and discussion

Subjects

Data Science