Tiêu đề BAI601-important-questions.pdf ✅

Module 1: Introduction & Language Modeling 1. Define NLP. Discuss its real-world applications. 2. List and explain the challenges faced in NLP. 3. What is the role of grammar in NLP? How is it different from language? 4. Explain the architecture of a statistical language model. 5. Derive and explain the unigram and bigram models with examples. 6. What is the Paninian Framework? Discuss its role in processing Indian languages. 7. Describe Karaka Theory and its relevance to Indian NLP. 8. Compare rule-based and statistical approaches to NLP. Module 2: Word-Level and Syntactic Analysis 1. Explain the use of regular expressions in NLP with examples. 2. Describe Finite-State Automata and its role in morphological parsing. 3. Explain morphological parsing with a focus on stemming and lemmatization. 4. What is spelling error detection and correction? Explain common techniques. 5. Define POS tagging. Compare rule-based, statistical, and hybrid approaches. 6. Write the CFG rules for a sentence and parse it using top-down parsing. 7. What is bottom-up parsing? How does it differ from top-down parsing? 8. Explain the CYK (Cocke–Younger–Kasami) parsing algorithm with a suitable example. Module 3: Naive Bayes, Text Classification & Sentiment Analysis 1. Explain the Naive Bayes classification algorithm in detail. 2. How do you train a Naive Bayes classifier? Illustrate with an example. 3. Describe the use of Naive Bayes in sentiment analysis. 4. What is add-1 (Laplace) smoothing? Why is it used? 5. Explain the text classification pipeline using Naive Bayes. IMPORTANT QUESTIONS Natural Language Processing BAI601