Speech and Natural Language Processing | UTU PREVIOUS YEAR QUESTION PAPER

 Speech and Natural Language Processing QUESTION PAPER

Speech and Natural Language Processing UTU QUESTION PAPER

BCST-803A/BITT-803A 

Roll No.

Even Semester Examination, 2021-22

Course Name : B.TECH

Branch: Computer Science

Semester: VIII

Subject: Speech and Natural Language Processing

Time: 3 Hours

Note: Attempt all questions: All Questions carry equal marks

Max Marks: 100

Number of Printed pages: 2

Q1. Attempt any four parts of the following (5 x 4 = 20)

(a) List and explain the challenges of Natural Language Processing.

(b) What is meant by Lexicon? How is it useful in NLP?

(c) Distinguish between semantics, pragmatics and discourse.

(d) Identify the morphological type (Noun phrase, Verb Phrase, Adjective Phrase) of following sentence segments

    1. important to Bill

    2. Looked up the tree

(e) Explain the role of transformational rules in transformational grammar with the help of an example.

Q2. Attempt any four parts of the following (5 x 4 = 20)

(a) With a neat diagram describe how a typical NLP system is organized?

(b) What are the 2 main classes of tagging algorithms in which they can be grouped into? Explain each one in detail.

(c) How the natural language processing systems are evaluated? Explain.

(d) List the problems associated with N-gram model. Explain how these problems are handled. (e) Illustrate parts of Speech Tagging and explain different categories of POS tagging.

Q3. Attempt any two parts of the following (10 x 2 = 20)

(a) Write an algorithm for parsing a finite-state transducer using the pseudo code. Explain the algorithm with an example. Also give the merits and demerits of this algorithm..

(b) Analyze the naive Bayes classifier approach to Word Sense Disambiguation in NLP.

(c) Discuss the following:

(i) Language as a rule-based system. (ii) Stochastic Part-of-Speech tagging.

Q4. Attempt any two parts of the following: (10 x 2 = 20)

(a) Write short notes on the following:

    (i) Text planning

    (ii) Goals of NLP

    (iii) Lexicons

    (iv) Applications of NLP

(b) Differentiate between a dialogue and the monologue. Give relevant examples for each scenario. (c) Explain text summarization and multiple document text summarizations with neat diagram.

Q5. Attempt any two parts of the following: (10 x 2 = 20)

(a) Describe different ways of building belief models in a conversational agent.

(b) Give an algorithm for pronoun resolution and explain it with an example.

(c) i) Describe transfer model of Machine Translation. List out its three phases.

    ii) Explain direct machine translation.