Gujarat Technological UniversitySummer 2025 examination

GTU 3170723 Natural Language Processing (NLP) Summer 2025 Question Paper & PDF

B.E. · Computer Engineering · Semester 7 · Subject code 3170723
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Total
70 marks
To pass
23 marks (33%)
Duration
2.5 Hours 2:30 PM – 5:00 PM
Questions
5 with internal choice
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16 of 25 questions on this paper were also asked in other years. 1 more is a type of question GTU sets again with new data. Each is marked below with the years it appeared.See all 21 repeated 3170723 questions, most asked first

Q.1

14 Marks
(a)
3 marksAsked in 3 of 3 papers
List out the main components of NLP and briefly describe them.

Also asked in Summer 2026 Q1a, Winter 2025 Q1a

(b)
4 marksAsked in 2 of 3 papers
Define NLP. Discuss three applications of NLP in real-world scenarios.

Also asked in Summer 2026 Q1b

(c)
7 marksAsked in 3 of 3 papers
Describe the process of building NLP pipeline with an example.

Also asked in Summer 2026 Q1c, Winter 2025 Q1c

Q.2

14 Marks
(a)
3 marks

Explain the concept of a Unigram Language Model. How does it differ from other N-gram models?

(b)
4 marksAsked in 3 of 3 papers

Describe the process of Part of Speech (POS) tagging and its significance in NLP.

Also asked in Summer 2026 Q2b, Winter 2025 Q2b

(c)
7 marksAsked in 2 of 3 papers

Explain in detail the different smoothing techniques used in language modeling. Provide examples to illustrate their effectiveness.

Also asked in Winter 2025 Q2c

(c)
7 marksAsked in 3 of 3 papers

Explain the importance of Named Entity Recognition (NER) in NLP. How does it work, and what challenges are faced when implementing NER systems?

Also asked in Summer 2026 Q3a, Winter 2025 Q4b

Q.3

14 Marks
(a)
3 marks
Briefly discuss one application of language modelling in NLP.
(b)
4 marksSame type asked in 3 of 3 papers
Explain how a bigram model predicts the next word in a sequence.

Also asked in Summer 2026 Q2c, Summer 2026 Q2c OR, Winter 2025 Q2c OR

(c)
7 marks

Discuss the concept of Word Sense Disambiguation (WSD) and the challenges involved in accurately identifying word senses.

(a)
3 marksAsked in 2 of 3 papers
Briefly describe lexical semantics in NLP.

Also asked in Winter 2025 Q3b OR

(b)
4 marksAsked in 2 of 3 papers

Describe the difference between knowledge-based and supervised approaches for Word Sense Disambiguation (WSD).

Also asked in Summer 2026 Q3c OR

(c)
7 marksAsked in 2 of 3 papers

Explain in detail the skip-gram and Continuous Bag-Of-Words (CBOW) models, highlighting how they are used for training word embeddings and comparing their effectiveness.

Also asked in Winter 2025 Q3a, Winter 2025 Q3b

Q.4

14 Marks
(a)
3 marksAsked in 3 of 3 papers
Briefly describe relation extraction in NLP.

Also asked in Summer 2026 Q4c OR, Winter 2025 Q4b OR

(b)
4 marksAsked in 2 of 3 papers

Briefly explain the concepts of text classification and text summarization in NLP.

Also asked in Winter 2025 Q4a OR

(c)
7 marksAsked in 3 of 3 papers
Discuss the various techniques used in Sentiment Mining.

Also asked in Summer 2026 Q4a, Winter 2025 Q4a

(a)
3 marks
What is the role of Named Entity Recognition in Information Extraction?
(b)
4 marks
How does question-answering work in a multilingual setting?
(c)
7 marksAsked in 3 of 3 papers
Explain the concept of Cross-Lingual Information Retrieval.

Also asked in Summer 2026 Q4b, Winter 2025 Q4c

Q.5

14 Marks
(a)
3 marks
Why is Machine Translation (MT) important in today’s world?
(b)
4 marks
Briefly discuss Statistical Machine Translation (SMT).
(c)
7 marksAsked in 3 of 3 papers
Explain neural machine translation with an example.

Also asked in Summer 2026 Q5b OR, Winter 2025 Q5b

(a)
3 marksAsked in 2 of 3 papers
What are some common challenges faced in Machine Translation?

Also asked in Winter 2025 Q5a

(b)
4 marks
Briefly describe the Direct Machine Translation approach.
(c)
7 marksAsked in 2 of 3 papers

Explain with an illustration how parameter learning in Statistical Machine Translation (SMT) is conducted.

Also asked in Winter 2025 Q5c

More Natural Language Processing papers

Questions repeat across sessions. Working through two or three papers shows you which topics GTU asks most.

Summer 202670 marks · 5 questions
Winter 202570 marks · 5 questions

Other Semester 7 subjects

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2170701
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2170709
Information and Network Security
2170710
Mobile Computing and Wireless Communication
2170714
Distributed DBMS

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About this paper

Official Gujarat Technological University (GTU) examination paper for Natural Language Processing (NLP) (Summer 2025, B.E. · Computer Engineering, Sem 7). Features complete 70-mark regular & remedial examination pattern, official marking distribution across all 5 questions, and direct 1-click official PDF download.

Transcribed for student exam preparation from Gujarat Technological University official examination archives. Questions, syllabus guidelines, and curriculum marking schemes remain the intellectual property of Gujarat Technological University.

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