Total No. of Questions : 8] SEAT No.
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P6566 [Total No. of Pages : 2
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B.E. (Computer Engineering)
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NATURAL LANGUAGE PROCESSING
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(2019 Pattern) (Semester - VIII) (410252A) (Elective - V)
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Time : 2½ Hours] [Max. Marks : 70
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Instructions to the candidates:
1) All questions are compulsory.
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2) Figures to the right indicate full marks.
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Q1) a) What are generative models of language? Explain any one model in detail.[4]
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b) Consider the following small corpus: [8]
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Training corpus:
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<s> I am from Pune </s>
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<s> I am a teacher </s>
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<s> students are good and are from various cities </s>
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<s> students from Pune do engineering </s>
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Test data:
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<s> students are from Pune </s>
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Find the Bigram probability of the given test sentence.
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c) Explain in detail Latent Semantic Analysis for topic modelling (LSA).[6]
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Q2) a) Write short note on BERT. [4]
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b) Given a document-term matrix with the following counts: [6]
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Document 1 Document 2 Document 3
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Term 1 10 5 0
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Term 2 2 0 8
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Term 3 1 3 6
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Calculate the TF-IDF score of “Term 1” in “Document 1”.
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c) Describe the Latent Dirichlet Allocation (LDA) algorithm and how it is
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used for topic modeling? [8]
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P.T.O.
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Q3) a) Describe the concept of Information Retrieval. Explain the significance
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of Natural Language Processing in Information Retrieval. [4]
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b) Explain reference resolution and conference resolution with example.[8]
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c) What is Cross-Lingual information Retrieval, and how is it used in Natural
Language Processing? Provide an example. [6]
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Q4) a) Explain the concept of the Vector Space Model, and describe how it is
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used in Information Retrieval. [6]
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Describe entity extraction and relation extraction with the help of examples.[8]
c) What is Named Entity Recognition (NER)? Describe the various metrics
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used for evaluation. [4]
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Q5) a) List the tools available for the development of NLP applications? Write
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the features of any 3 tools. [7]
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b) Describe in detail the Lesk algorithm and Walker’s algorithm for word
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sense disambiguation. [10]
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Q6) a)
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Explain the following lexical knowledge networks? [10]
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i) WordNet
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ii) Indo WordNet
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iii) VerbNets
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iv) PropBank
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v) Treebanks
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b) Write Python code using NLTK library to split the text into tokens using
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whitespace, punctuation-based and default tokenization methods. [7]
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Q7) a) Explain three stages of Question Answering system with neat diagram.[7]
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b) Explain Rule based Machine Translation and Statistical Machine Translation
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(SMT) with suitable diagrams and example. [10]
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Q8) a) Describe following NLP applications: [10]
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i) Text Entailment
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ii) Dialog and Conversational Agents
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b) Explain Natural Language Generation with reference architecture. [7]
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