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Download JNTUH MCA 5th Sem R13 2017 August 815BJ Information Retrieval Systems Question Paper

Download JNTUH (Jawaharlal nehru technological university) MCA (Master of Computer Applications) 5th Sem (Fifth Semester) Regulation-R13 2017 August 815BJ Information Retrieval Systems Previous Question Paper

This post was last modified on 17 March 2023





R13

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Code No: 815BJ

JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

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MCA V Semester Examinations, August - 2017

INFORMATION RETRIEVAL SYSTEMS


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Time: 3 Hours



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Max. Marks: 60

Note: This question paper contains two parts A and B.

Part A is compulsory which carries 20 marks. Answer all questions in Part A. Part B

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consists of 5 Units. Answer any one full question from each unit. Each question carries
8 marks and may have a, b, c as sub questions.



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PART - A




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5 ? 4 Marks = 20

1.a) What is IR and provide an example for IR problem?


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[4]

b) Describe the procedure for ,,pseudo relevance feedback.


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[4]

c) Provide some basic XML concepts.


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[4]

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d) What is "Low rank approximations" in matrix decompositions?



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[4]

e) What is the role of Web graphs in link analysis?

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[4]

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PART - B

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5 ? 8 Marks = 40


2.

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How do we process a query using an inverted index and the basic Boolean retrieval
model? Explain with example.


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[8]

OR

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3.

Explain the following spelling correction strategies in brief.


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a) K-gram indexes b) context sensitive.



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[4+4]

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4.

Write short notes on the following "parametric and zone indexes".

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a) Weighted zone scoring

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b) The optimal weighting.




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[4+4]

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OR

5.

Describe "Rocchio algorithm for relevance feedback".

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[8]


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6.a)

Compare text-centric and data-centric XML retrieval.

b)

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What is "The 1/0 loss case" probability ranking principle?



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[4+4]

OR

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7.

Describe about "Query likelihood models in language models" for Information retrieval.

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[8]

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8.

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Write about the following models of extended support vector machines.



a) Soft margin classification b) Multi class SVMs.

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[4+4]

OR

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9.

Describe the following Hierarchical clustering methods.

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a) Centroid clustering

b) Divisive Clustering.

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[4+4]


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10.

Explain the role of "Index size and clustering" in approximation of web search
comprehensiveness.

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[8]

OR

11.

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What is Web Crawling? Draw and explain the basic Web Crawler architecture.

[8]

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