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EndsemMay 20252019 patternBE Comp

Natural Language Processing (NLP) SPPU Endsem Question Paper May 2025

All 8 questions from this SPPU endsem paper, as printed. Read them here or open the PDF.

Open the PDFMost repeated NLP questions
Subject code
410252(A)
Pattern
2019 pattern
Exam
Endsem
Session
May 2025
Max. marks
70
Time
2½ Hours
Semester
VIII
Paper code
[6404]-96

Printed for: B. E. (Computer Engineering)

Instructions to the candidates

  1. Solve Q.1 or Q.2, Q.3 or Q.4, Q.5 or Q.6, 7 or Q.8.
  2. Neat diagrams must be drawn wherever necessary.
  3. Figures to the right indicate full marks.

Questions

  1. Q1

    1. a)

      Explain the concept of log-linear models in NLP. How do these models work, and what are some common applications of this approach?

      [6]
    2. b)

      Explain the concept of doc2vec and its use in generating document embeddings. Discuss the differences between doc2vec and word2vec.

      [8]
    3. c)

      Explain Non-Negative Matrix Factorization (NMF) in the context of topic modeling

      [4]

  2. Q2

    1. a)

      What is a Markov model, and how is it used to generate language sequences? Describe the process of training and using a Markov model for language generation.

      [6]
    2. b)

      Explain the concept of Latent Semantic Analysis (LSA) and how it is used for identifying relationships between words.

      [8]
    3. c)

      Write a short note on TF-IDF (Term Frequency-Inverse Document Frequency) representation.

      [4]

  3. Q3

    1. a)

      Explain the concept of reference resolution and coreference resolution in NLP. How do these techniques help in understanding the relationships between entities in a text? Provide examples.

      [8]
    2. b)

      Explain the concept of Cross-Lingual Information Retrieval (CLIR). Discuss the challenges of retrieving information across languages, and what techniques or models are commonly used to address these challenges?

      [8]
    3. c)

      What is Information Retrieval, and how is it used in NLP? 2

      [2]

  4. Q4

    1. a)

      Explain the process of Entity Extraction in Information Retrieval. Discuss the various techniques and algorithms used for Entity Extraction.

      [8]
    2. b)

      Describe the Vector Space Model (VSM) for information retrieval. How does VSM represent documents and queries, and how are similarities calculated? Discuss the strengths and weaknesses of VSM.

      Also asked:May 2024

      [8]
    3. c)

      What is Named Entity Recognition (NER)?

      [2]

  5. Q5

    1. a)

      Explain how NLTK, spaCy, and Gensim handle tokenization and part- of-speech tagging. Discuss the advantages and disadvantages of each library’s approach.

      [9]
    2. b)

      Explain the concept of WordNet as a lexical knowledge network. How can WordNet be used in natural language processing, and what are its limitations?

      [8]

  6. Q6

    1. a)

      Discuss the role of Treebanks and Universal Dependency Treebanks in NLP research. How do they contribute to syntactic analysis and parsing in various languages?

      [9]
    2. b)

      Explain the Lesk Algorithm for word sense disambiguation . How does it work, and what are its advantages and limitations?

      [8]

  7. Q7

    1. a)

      Explain the key principles of rule-based machine translation. How do rule-based techniques differ from statistical approaches in machine translation? Provide an example of a rule-based translation.

      Also asked:May 2024

      [9]
    2. b)

      Explain the fundamental components of a question-answering system. How do question answering systems utilize machine learning and NLP techniques to find accurate answers to questions?

      [8]

  8. Q8

    1. a)

      Define text entailment and describe its importance in natural language understanding. How does text entailment differ from text similarity or paraphrasing? Provide an example of text entailment.

      [9]
    2. b)

      Explain Natural Language Generation with reference architecture. 

      Also asked:Nov 2025 ·Nov 2024 ·Nov 2023 ·May 2023

      [8]