AKTU MCA Natural Language Processing (KCA053) Question Paper 2024-25

Note: Attempt all Sections. In case of any missing data, choose suitably.

Section A

Q1. Attempt all questions in brief · 2 × 10 = 20 marks

1. List the applications ofNLP.
2. Explain open and closed word classes in English language.
3. Explain knowledge representation.
4. Differentiate between syntax and semantics.
5. Describethe parsing process.
6. Discuss is the function of the lexicon during parsing?
7. Discuss Auxiliary verb with suitable example.
8. Explain different ways to create words from morphemes
9. Explain the use of language model?
10. What is meant by Part-of-Speech (POS) tagging?

Section B

Q2. Attempt any three of the following · 10 × 3 = 30 marks

1. Explain the organization of natural language understanding system.
2. Discuss the language generation with respect to machine translation.
3. Describe various syntactic parsing techniques.
4. Determine context free grammar. Discuss its use and limitations.
5. Discuss thedetails about the encoding ambiguity in logical form.

Section C

Q3

Attempt any one part of the following · 10 × 1 = 10 marks

1. Explain the different steps in natural language understanding in detail.
2. Explain the different levels of language analysis process.

Q4

Attempt any one part of the following · 10 × 1 = 10 marks

1. Explain top-down parsing and bottom-up parsing with an example.
2. Explain all the steps that are used in machine translation.

Q5

Attempt any one part of the following · 10 × 1 = 10 marks

1. Discuss morphological analysis in detail.
2. Discuss transition network. Explain the different types of transition network.

Q6

Attempt any one part of the following · 10 × 1 = 10 marks

1. Illustrate human preferences in parsing in detail.
2. Illustrate lexical analysis. Explain in brief. Also explain finite state automaton.

Q7

Attempt any one part of the following · 10 × 1 = 10 marks

1. Describe is semantic analysis? Discuss different semantic relationships between the words.
2. Describe generative models and compare itwith PCFG model.

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