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Download AKTU B-Tech 8th Sem 2016-17 NIT080 Natural Language Processing Question Paper

Download AKTU (Dr. A.P.J. Abdul Kalam Technical University (AKTU), formerly Uttar Pradesh Technical University (UPTU) B-Tech 8th Semester (Eight Semester) 2016-17 NIT080 Natural Language Processing Question Paper

This post was last modified on 30 January 2020

AKTU B-Tech Last 10 Years 2010-2020 Previous Question Papers || Dr. A.P.J. Abdul Kalam Technical University


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B.TECH.

THEORY EXAMINATION (SEM–VIII) 2016-17

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NATURAL LANGUAGE PROCESSING

Time: 3 Hours

Max. Marks : 100

Note: Be precise in your answer. In case of numerical problem assume data wherever not provided.

SECTION-A

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  1. Explain the following:

    1. Issues and problems in NLP
    2. Lexicon
    3. Advantages of speech understanding system
    4. Left associative grammars
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    6. Best first parsing
    7. Scope of ambiguity in natural languages
    8. Differentiate syntax and semantics
    9. Probabilistic context-free grammars
    10. Stochastic Part-of-Speech tagging
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    12. Elements of a language

    (10×2=20)

SECTION-B

(10×5=50)

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  1. Attempt any five of the following:

    1. How the natural language processing systems are evaluated? Explain.
    2. What is the need of NLP system? Why is it difficult to process the natural languages?
    3. What are the issues in parsing? Describe various techniques used for parsing with a suitable example.
    4. What are the differences between natural language processing and natural language understanding?
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    6. Explain in detail about the organization of NLP systems.
    7. What are the different ways in which a speech dialogue system can reduce the number of errors made because of incorrect speech recognition?
    8. How the man-machine interface is designed using NLP techniques? Explain with example.
    9. What are the different levels in language analysis? Explain with examples.
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SECTION-C

Attempt any two of the following: (15×2=30)

  1. How the knowledge is represented using semantic networks? Also explain how information is deduced from semantic networks.
  2. Give an example of an NLP system or approach that only knows about individual words (not about their context). Explain what knowledge of words it needs and given an overview of how it works. Give examples to illustrate your explanation.
  3. Write an algorithm for converting an arbitrary context-free grammar into Chomsky normal form. Explain it with suitable examples.
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This download link is referred from the post: AKTU B-Tech Last 10 Years 2010-2020 Previous Question Papers || Dr. A.P.J. Abdul Kalam Technical University

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