Question 1
(a)
List out the main components of NLP and briefly describe them.
3 Marks
(b)
Define NLP. Discuss three applications of NLP in real-world scenarios.
4 Marks
(c)
Describe the process of building NLP pipeline with an example.
7 Marks
Gujarat Technological UniversitySummer 2025 Examination
GTU 3170723 Natural Language Processing (NLP) Summer 2025 Paper Solution & PDF
B.E. · Computer Engineering · Semester 7 · Subject Code: 3170723
Total Marks70 MarksExternal theory exam
Passing Marks23 Marks33% minimum cutoff
Exam Duration2.5 Hours2:30 PM – 5:00 PM
Paper Structure5 QuestionsWith internal OR choices
Question 2
(a)
Explain the concept of a Unigram Language Model. How does it differ from other N-gram models?
3 Marks
(b)
Describe the process of Part of Speech (POS) tagging and its significance in NLP.
4 Marks
(c)
Explain in detail the different smoothing techniques used in language modeling. Provide examples to illustrate their effectiveness.
7 Marks
OR OPTION
(c)
Explain the importance of Named Entity Recognition (NER) in NLP. How does it work, and what challenges are faced when implementing NER systems?
7 Marks
Question 3
(a)
Briefly discuss one application of language modelling in NLP.
3 Marks
(b)
Explain how a bigram model predicts the next word in a sequence.
4 Marks
(c)
Discuss the concept of Word Sense Disambiguation (WSD) and the challenges involved in accurately identifying word senses.
7 Marks
OR OPTION
(a)
Briefly describe lexical semantics in NLP.
3 Marks
(b)
Describe the difference between knowledge-based and supervised approaches for Word Sense Disambiguation (WSD).
4 Marks
(c)
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.
7 Marks
Question 4
(a)
Briefly describe relation extraction in NLP.
3 Marks
(b)
Briefly explain the concepts of text classification and text summarization in NLP.
4 Marks
(c)
Discuss the various techniques used in Sentiment Mining.
7 Marks
OR OPTION
(a)
What is the role of Named Entity Recognition in Information Extraction?
3 Marks
(b)
How does question-answering work in a multilingual setting?
4 Marks
(c)
Explain the concept of Cross-Lingual Information Retrieval.
7 Marks
Question 5
(a)
Why is Machine Translation (MT) important in today’s world?
3 Marks
(b)
Briefly discuss Statistical Machine Translation (SMT).
4 Marks
(c)
Explain neural machine translation with an example.
7 Marks
OR OPTION
(a)
What are some common challenges faced in Machine Translation?
3 Marks
(b)
Briefly describe the Direct Machine Translation approach.
4 Marks
(c)
Explain with an illustration how parameter learning in Statistical Machine Translation (SMT) is conducted.
7 Marks
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About this Examination Paper & Attribution
Official Gujarat Technological University (GTU) examination paper and step-by-step solutions 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.