Showing posts with label paper. Show all posts
Showing posts with label paper. Show all posts

Tuesday, January 24, 2023

Midsemester - Information Retrieval -- DSECLZG537 - Jan 7th 2023

Information Retrieval - Regular - Mid Semester conducted on 7th Jan 2023









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Sunday, January 1, 2023

Information Retrieval -- DSECLZG537 - Mid Semester Question Paper - June 2021

 

Birla Institute of Technology & Science, Pilani

Work-Integrated Learning Programmes Division

June 2021

Mid-Semester Test

(EC-1 Regular)

Text Box: No. of Pages        = 2
No. of Questions = 2

 


Course No.                   : SS ZG537  

Course Title                  : INFORMATION RETRIEVAL  

Nature of Exam            : Closed Book

Weightage                    : 30%

 

Note:

1.       Please follow all the Instructions to Candidates given on the cover page of the answer book.

2.       All parts of a question should be answered consecutively. Each answer should start from a fresh page. 

3.       Assumptions made if any, should be stated clearly at the beginning of your answer.

 

Q1 – 2+5+3+5=15 Marks

A) Give an example of uncertainty and vagueness issues in Information retrieval [2 Marks]               

 

B) Explain the merge algorithm for the query “Information Retrieval”? What is the best order for query processing for the query “BITS AND Information AND Retrieval”? What Documents will be returned as output from the 15 documents? [5 Marks]



 


Solution:

Merge Algorithm - Intersecting two posting lists : Algorithm


Output document - 11

 

C) [3 Marks]

 

D) Build inverted index using Blocked sort-based Indexing for 50 million records. Explain the algorithm in detail with respect to indexing 50 million records.                            [5 Marks]

 

 

Q2 – 5+5+5=15 Marks

A)    Assume a corpus of 10000 documents.  The following table gives the TF and DF values for the 3 terms in the corpus of documents. Calculate the logarithmic TF-IDF values.                                                                                                           [5 Marks]

 

Term

Doc1

Doc2

Doc3

bits

15

5

20

pilani

2

20

0

mtech

0

20

15

 

Term

dft

 

bits

2000

pilani

1500

mtech

500

 

 

 

 

 

B) Classify the test document d6 into c1 or c2 using naïve bayes classifier. The documents in the training set and the appropriate class label is given below.  [5 Marks]

                                                                                     

 

 

Docid

Words in document

c= c1

c= c2

Training Set

d1

positive

Yes

No

 

d2

Very positive

Yes

No

 

d3

Positive very positive 

Yes

No

 

d4

very negative

No

Yes

 

d5

negative

No

Yes

Test Set

d6

Negative positive very positive

?

?

 

C) The search engine ranked results on 0-5 relevance scale: 2, 2, 3, 0, 5. Calculate the NDCG metric for the same. [5 Marks]

                                                                                                                      

 





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