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Course Content
MODULE 1
0/8
1. What is NLP? List and explain applications of NLP
15:33
2. List and explain different phases of analysis in Natural Language Processing with an example for each.
07:22
3. Describe C-structure and f-structure in LFG
06:28
4. Construct the Surface structure and Deep Structure for the following sentences:
05:06
5. Write C-structure and f-structure for the sentence ‘She saw stars’ using the CFG rules as below:
06:00
6. Explain the role of transformational rules in transformational grammar with the help of an example?
03:32
7. Find the probability of test sentence given below if a bigram model is used on the following training sentences:
06:37
8. Describe the Paninian framework for Indian languages. Explain the layered representation of Paninian Grammar and Karaka theory
10:48
MODULE 2
0/8
Q1.WHAT IS MORPHOLOGICAL PARSING? EXPLAIN THE 2-LEVEL MORPHOLOGICAL MODEL WITH AN EXAMPLE.
06:24
Q2. DEVELOP AN FST-BASED MORPHOLOGICAL PARSER FOR SINGULAR AND PLURAL NOUNS IN ENGLISH.
06:48
Q3. EXPLAIN MINIMUM EDIT DISTANCE ALGORITHM AND FIND MINIMUM EDIT DISTANCE BETWEEN GIVEN TWO STRINGS
06:49
Q4. EXPLAIN RULE BASED AND STOCHASTIC TAGGERS
06:12
Q5. WHAT IS PROBABILISTIC PARSING AND PROBABILISTIC LANGUAGE PROCESSING
04:01
Q6. WHAT IS THE DIFFERENCE BETWEEN SEMANTIC PARSING AND SYNTAX PARSING?
03:22
Q7. CONSTRUCT THE PARSE TREE FOR THE SENTENCE “THE CAT SAT ON THE MAT.”
03:56
Q8.CONSIDER THE FOLLOWING SENTENCES: “I SHOT AN ELEPHANT IN MY PAJAMAS”. CHECK THE AMBIGUITY USING GIVEN PRODUCTIONS:
03:31
MODULE 3
0/7
Q1. EXPLAIN NAÏVE BAYES CLASSIFIER WITH EXAMPLE ?
05:39
Q2. HOW DO NAIVE BAYES ALGORITHMS WORK? EXPLAIN WITH EXAMPLE.
03:53
Q3. EXPLAIN BAG OF WORDS MODEL?
04:32
Q4. EXPLAIN SPAM CLASSIFICATION USING DECISION RULE?
03:33
Q5. DESCRIBE THE FOLLOWING: A) CLASS-CONDITIONAL PROBABILITIES B) PRIOR PROBABILITIES C) EVIDENCE
08:01
Q6. EXPLAIN MULTINOMIAL NAIVE BAYES WITH A TOY EXAMPLE
02:34
Q7. EXPLAIN THE WORKING OF NAÏVE BAYES AS LANGUAGE MODEL.
05:17
MODULE 4
0/5
Q1.EXPLAIN DESIGN FEATURES OF INFORMATION RETRIEVAL WITH NEAT DIAGRAM.
06:45
Q2. EXPLAIN CLASSICAL MODELS OF IR IN DETAIL
06:15
Q3. EXPLAIN NON-CLASSICAL MODELS OF IR IN DETAIL
03:20
Q4. DESCRIBE ALTERNATIVE MODELS OF IR IN DETAIL: A) CLUSTER MODEL B) FUZZY MODEL C) LSTM MODEL
03:20
Q5.EXPALIN 1)WORDNET 2)FRAMENET 3) STEMMERS
19:01
MODULE 5
0/5
Q1. WHAT IS A MACHINE TRANSLATION, AND HOW DOES IT WORK?
06:25
Q2. EXPLAIN LANGUAGE DIVERGENCES AND TYPOLOGY WITH EXAMPLE
09:02
Q3.DESCRIBE MACHINE TRANSLATION USING ENCODER-DECODER.
07:42
Q4. DESCRIBE LOW-RESOURCE LANGUAGES IN MACHINE TRANSLATION.
06:38
Q5. EXPLAIN A) MT EVALUATION B) BIAS C) ETHICAL ISSUES
06:09
NATURAL LANGUAGE PROCESSING || CSE stream || Very Important Questions Explained with Concepts || All 5 modules || BAD613B || Last moment exam preparation
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