MSCS_AIE 532:Natural Language Processing
Course Content
Week 1: Introduction to Natural Language Processing
-
LO1: Define the fundamental concepts of natural language processing and its role in enabling computers to process human language data.
-
LO2: Describe major challenges involved in handling human language, including ambiguity, context understanding, and language variability.
-
LO3: Explain important NLP applications across different domains, including healthcare, search systems, and intelligent assistants
-
Multiple Choice Questions
-
True/False
-
Scenario Based Multiple Choice Questions
-
Key Terms and Concepts Questions
-
Short Answer Questions
-
Written Assignment
-
Presentation Task
-
Role-Playing Activity
-
Exercises and Activities Adaptation
-
Peer Review Task
Week 2: Text Preprocessing
-
LO1: Explain the importance of text preprocessing in preparing raw language data for NLP tasks.
-
LO2: Apply tokenization, stemming, lemmatization, and stop-word removal techniques to transform text into usable representations.
-
LO3: Analyze the impact of preprocessing methods on the quality of NLP models and text analysis results.
-
Multiple Choice Questions
-
True/False
-
Scenario Based Multiple Choice Questions
-
Key Terms and Concepts Questions
-
Short Answer Questions
-
Written Assignment
-
Presentation Task
-
Role-Playing Activity
-
Exercises and Activities Adaptation
-
Peer Review Task
Week 3: Language Models
-
LO1: Describe the structure and purpose of language models used for predicting and generating text sequences.
-
LO2: Explain N-gram models, smoothing techniques, and probability-based approaches for statistical language processing.
-
LO3: Evaluate the effectiveness of different language modeling approaches for various NLP applications.
-
Multiple Choice Questions
-
True/False
-
Scenario Based Multiple Choice Questions
-
Key Terms and Concepts Questions
-
Short Answer Questions
-
Written Assignment
-
Presentation Task
-
Role-Playing Activity
-
Exercises and Activities Adaptation
-
Peer Review Task
Week 4: Syntax and Parsing
-
LO1: Explain the role of syntax analysis in understanding the grammatical structure of natural language.
-
LO2: Apply part-of-speech tagging and context-free grammar concepts to analyze sentence structures.
-
LO3: Analyze parsing techniques used to identify relationships between words and phrases in sentences.
-
Multiple Choice Questions
-
True/False
-
Scenario Based Multiple Choice Questions
-
Key Terms and Concepts Questions
-
Short Answer Questions
-
Written Assignment
-
Presentation Task
-
Role-Playing Activity
-
Exercises and Activities Adaptation
-
Peer Review Task
Week 5: Semantic Analysis
-
LO1: Describe semantic analysis methods used to determine meaning from textual information.
-
LO2: Explain named entity recognition, word sense disambiguation, and semantic role labeling techniques.
-
LO3: Analyze how semantic processing improves language understanding in NLP systems.
-
Multiple Choice Questions
-
True/False
-
Scenario Based Multiple Choice Questions
-
Key Terms and Concepts Questions
-
Short Answer Questions
-
Written Assignment
-
Presentation Task
-
Role-Playing Activity
-
Exercises and Activities Adaptation
-
Peer Review Task
Week 6: Machine Translation
-
LO1: Explain the principles of machine translation and its role in multilingual communication.
-
LO2: Describe rule-based, statistical, and neural approaches used for language translation.
-
LO3: Evaluate translation approaches based on accuracy, efficiency, and practical applications.
-
Multiple Choice Questions
-
True/False
-
Scenario Based Multiple Choice Questions
-
Key Terms and Concepts Questions
-
Short Answer Questions
-
Written Assignment
-
Presentation Task
-
Role-Playing Activity
-
Exercises and Activities Adaptation
-
Peer Review Task
Week 7: Sentiment Analysis
-
LO1: Define sentiment analysis concepts and their importance in extracting opinions from text data.
-
LO2: Describe techniques used for polarity detection and opinion mining in NLP applications.
-
LO3: Analyze sentiment analysis approaches for understanding user feedback and social media opinions.
-
Multiple Choice Questions
-
True/False
-
Scenario Based Multiple Choice Questions
-
Key Terms and Concepts Questions
-
Short Answer Questions
-
Written Assignment
-
Presentation Task
-
Role-Playing Activity
-
Exercises and Activities Adaptation
-
Peer Review Task
Week 8: Midterm Test Or Assignment
Week 9: Question Answering Systems
-
LO1: Explain the components and workflow of question answering systems
-
LO2: Describe information retrieval and response generation techniques used in answering user queries.
-
LO3: Analyze methods for developing question answering systems across different application areas.
-
Multiple Choice Questions
-
True/False
-
Scenario Based Multiple Choice Questions
-
Key Terms and Concepts Questions
-
Short Answer Questions
-
Written Assignment
-
Presentation Task
-
Role-Playing Activity
-
Exercises and Activities Adaptation
-
Peer Review Task
Week 10: Deep Learning for NLP
-
LO1: Describe the role of deep learning techniques in modern NLP applications.
-
LO2: Explain word embeddings, recurrent neural networks, and transformer-based architectures.
-
LO3: Analyze how deep learning models improve language representation and processing capabilities.
-
Multiple Choice Questions
-
True/False
-
Scenario Based Multiple Choice Questions
-
Key Terms and Concepts Questions
-
Short Answer Questions
-
Written Assignment
-
Presentation Task
-
Role-Playing Activity
-
Exercises and Activities Adaptation
-
Peer Review Task
Week 11: Text Classification
-
LO1: Explain the principles and workflow of text classification systems.
-
LO2: Apply classification techniques for tasks such as spam detection and topic categorization.
-
LO3: Evaluate text classification methods based on performance and application requirements.
-
Multiple Choice Questions
-
True/False
-
Scenario Based Multiple Choice Questions
-
Key Terms and Concepts Questions
-
Short Answer Questions
-
Written Assignment
-
Presentation Task
-
Role-Playing Activity
-
Exercises and Activities Adaptation
-
Peer Review Task
Week 12: Dialogue Systems and Chatbots
-
LO1: Describe the architecture and components of dialogue systems and conversational agents.
-
LO2: Explain techniques used for designing interactive chatbots and automated conversations.
-
LO3: Analyze chatbot development approaches for improving human-computer interaction.
-
Multiple Choice Questions
-
True/False
-
Scenario Based Multiple Choice Questions
-
Key Terms and Concepts Questions
-
Short Answer Questions
-
Written Assignment
-
Presentation Task
-
Role-Playing Activity
-
Exercises and Activities Adaptation
-
Peer Review Task
Week 13: Ethics in NLP
-
LO1: Explain ethical considerations associated with NLP technologies and applications.
-
LO2: Describe bias issues, privacy concerns, and responsible AI practices in language models.
-
LO3: Evaluate approaches for developing fair and reliable NLP systems.
-
Multiple Choice Questions
-
True/False
-
Scenario Based Multiple Choice Questions
-
Key Terms and Concepts Questions
-
Short Answer Questions
-
Written Assignment
-
Presentation Task
-
Role-Playing Activity
-
Exercises and Activities Adaptation
-
Peer Review Task
Week 14: Advanced Topics in NLP
-
LO1: Describe advanced NLP concepts including transfer learning, multilingual models, and low-resource language processing.
-
LO2: Explain how modern NLP models adapt knowledge across different languages and tasks.
-
LO3: Analyze challenges and opportunities associated with advanced NLP techniques.
-
Multiple Choice Questions
-
True/False
-
Scenario Based Multiple Choice Questions
-
Key Terms and Concepts Questions
-
Short Answer Questions
-
Written Assignment
-
Presentation Task
-
Role-Playing Activity
-
Exercises and Activities Adaptation
-
Peer Review Task
Week 15: Review and Final Preparation
-
LO1: Summarize key concepts, techniques, and applications covered throughout the NLP course.
-
LO2: Describe connections between NLP methods, machine learning approaches, and real-world language applications.
-
LO3: Evaluate different NLP approaches to prepare for solving practical language processing problems.
-
Multiple Choice Questions
-
True/False
-
Scenario Based Multiple Choice Questions
-
Key Terms and Concepts Questions
-
Short Answer Questions
-
Written Assignment
-
Presentation Task
-
Role-Playing Activity
-
Exercises and Activities Adaptation
-
Peer Review Task
