Computer Science (Information Retrieval)

CSE7337, Page 2 of 4

.

1. Instead of returning nothing for a query, a search engine should return some results even if they are incorrect.” Do you agree or disagree? Explain.

2. What are the differences between static and dynamic summaries? Describe a scenario where each type of summary would be the best solution to a search query.

3.

4. Consider an information need for which there are 6 relevant documents in the collection. Contrast two systems run on this collection. Their top 10 results are judged for relevance as follows:

a. Complete this table with numerical values:

	System 1	System 2
		Recall	Precision		Recall	Precision
1	R			N
2	N			R
3	R			N
4	N			N
5	N			R
6	R			R
7	N			R
8	N			N
9	R			N
10	R			N

b. What is the MAP value for each system? (equation okay)

c. What is F β=1 for each system with 10 documents returned?

5. True/False

a) _____ The tf-idf weight increases with the number of occurrences of a term within a document

b) _____ The tf-idf weight increases with the rarity of the term in the collection.

c) _____ The summary information displayed by a search engine must come from the “description” meta tag in a html file.

d) _____ Hard clustering is more common and easier than soft clustering.

e) _____ Pseudo relevance feedback is the same as indirect relevance feedback

f) _____ Query expansion means to double the number of results shown to the user

g) _____ Hierarchical agglomerative clustering is a “top down” clustering technique.

h) _____ Linear classifiers partition the dataspace into overlapping regions.

i) _____ K-means clustering is an example of unsupervised learning.

j) _____ Hub sites should be scored higher than authoritative sites.

0.80	0.70	0.90
0.00	0.50	0.10
0.50	0.75	0.25

6.The following probabilities have been Long Sweet Green determined from a training set of 1000. Cucumber

Given a sample that is long, sweet and green; Jalapeno what is the probability that it would be classified Other as a Cucumber using a naïve bayes classifier? Show your work.

7. Apply the KNN algorithm to classify the data item (14) with this known set of data and classes: { (10,1), (11,1), (15,2), (12,1), (18,2), (9,1), (20,2), (17,2) }. Show your work for K = 3 and K = 5.

8. Given this portion of a web graph

a) Show the node adjacency matrix

b) Convert the adjacency matrix to a transition probability matrix (i.e. Markov chain) for PageRank.

9. Complete the table below so that the cosine similarity for the query “brown cat” against document three is 1.0 ( SMART nnc.nnc ).

	q	𝑞𝑞⃗	d1	d2	d3	𝑑𝑑⃗1	𝑑𝑑⃗2	𝑑𝑑⃗3	𝑞𝑞⃗ • 𝑑𝑑⃗1	𝑞𝑞⃗ • 𝑑𝑑⃗2	𝑞𝑞⃗ • 𝑑𝑑⃗3
Brown	1	.707	2	1		.632	.277		.447	.196
Cat	1	.707	1	1		.316	.277		.223	.196
how	0	0	1	1		.316	.277		0	0
meow	0	0	0	3		0	.832		0	0
now	0	0	2	1		.632	.277		0	0

.670

.392

1.0

score