Free Printable IRT ASSESSMENT 1

1.

IR Stands for______________.

a)

Information Retrieval

b)

Information Retired

c)

Inform Retrieval

d)

Information Ready

2.

A model of information retrieval in which we can pose any query in which search terms are combined with the operators AND, OR, and NOT:

a)

Ad Hoc Retrieval

b)

Ranked Retrieval Model

c)

Boolean Information Model

d)

Proximity Query Model

3.

A data structure that maps terms back to the parts of a document in which they occur is called an

a)

Postings list

b)

Incidence Matrix

c)

Dictionary

d)

Inverted IndeX

4.

The model of information retrieval in which we can pose any query in the form of a Boolean expression is called the ranked retrieval model.

a)

True

b)

False

5.

The number of times that a word or term occurs in a document is called the:

a)

Proximity Operator

b)

Vocabulary Lexicon

c)

Term Frequency

d)

Indexing Granularity

6.

Stemming increases the size of the vocabulary.

a)

True

b)

False

7.

Weighted zone scoring is sometimes referred to as ranked Boolean retrieval.

a)

TRUE

b)

FALSE

8.

A scheme where a weight is assigned to a term based upon the number of occurrences of the term within a document is called:

a)

Bag of Words

b)

Document Frequency

c)

Term Frequency

d)

Optimal weight

9.

A metric derived by taking the log of N divided by the document frequency where N is the total number of documents in a collection is called:

a)

document frequency inverse

b)

tf-idf weight

c)

collection frequency

d)

Inverse document frequency

10.

The tf-idf weight is highest when a term t occurs many times within a small number of documents.

a)

True

b)

False

11.

A measure of similarity between two vectors which is determined by measuring the angle between them is called:

a)

cosine similarity

b)

sin similarity

c)

vector similarity

d)

vector scoring

12.

An approach to retrieval in a search that is likely (but not precisely) to produce the top K scoring documents is called:

a)

Exact top K document retrieval

b)

top scoring document retrieval

c)

Inexact top K document retrieval

d)

Imprecise top K document retrieval

13.

Ad hoc retrieval is a model of information retrieval in which we can pose any query in which search terms are combined with the operators AND, OR, and NOT.

a)

True

b)

False

14.

____________ is the process of selecting how to organize the work of answering a query so that the least total amount of work needs to be done by the system.

a)

Intersect

b)

Query optimization

c)

posting merge

d)

conjunctive query

15.

____________ has been used for dictionary lookup in some search engines.

a)

Hashing

b)

keys

c)

binary tree

d)

B-tree

16.

A ___________ on the dictionary is a convenient way of handling trailing wildcard queries.

a)

Hashing

b)

search tree

c)

B-tree

d)

reverse B-tree

17.

The first web search engine known to apply link analysis on a large scale was ____________.

a)

opera

b)

Google

c)

yahoo

d)

bing

18.

The first challenge in __________ is that users want us to return parts of documents, not entire documents as IR systems.

a)

structured retrieval

b)

unstructured retrieval

c)

indexing unit

d)

XML retrieval

19.

Early goals of the IR area is/are

a)

indexing text

b)

searching for useful documents in a collection

c)

Both A and B

d)

None

20.

In libraries, Specialized data structure is used for fast search called

a)

Heap

b)

Sorting

c)

Indexes

d)

None

21.

_________

coined the term Information retrieval

a)

Galvin

Moores

b)

Hans

Peter Luhn

c)

Eugene

Garfield

d)

Philip

Bagley

22.

__________ is the central importance of IR

a)

Information

b)

Relevance

c)

Data

d)

Text

23.

User

who has an interest that is either poorly defined or inherently broad, then

the user is said to perform ______

a)

Querying

b)

Searching

c)

Browsing

d)

Routing

24.

The task of determining which documents of a collection contain the keywords in the user query

a)

Information searching

b)

Information retrieval

c)

Data searching

d)

Data Retrieval

25.

____________

is used to identify the documents that are most likely to be considered relevant by the user and constitutes the most critical part of the IR

system

a)

Ranking

b)

Indexing

c)

Sorting

d)

Crawling

26.

The indexing terms are selected from the documents of the collection by applying text operations _________

a)

Eliminating Stop words

b)

Stemming

c)

Both A and B

d)

None

27.

For classic information retrieval models, the index term weights are assumed to be ________

a)

Mutually dependent

b)

Neither Independent nor Dependent

c)

Mutually Independent

d)

None of the above

28.

The probabilistic model tries ------------------

a)

to estimate the probability that a document will be relevant to a user query

b)

to assume that this probability depends on the query and document representations only

c)

Both A and B

d)

None

29.

The term-document relation represents

a)

frequency of the document

b)

frequency of the term

c)

frequency of the term in the document

d)

None

30.

In Neural network for IR, how many layer network is available

a)

2

b)

3

c)

4

d)

5

31.

Which is not the drawbacks of the Boolean Model

a)

Retrieval based on binary decision criteria with no notion of

partial matching

b)

No ranking of the documents is provided

c)

The Boolean queries formulated by the users are most

often too simplistic

d)

None

32.

Probabilistic IR Models based on ___ Rule

a)

Joint probability

b)

Bayes’ Rule

c)

Conditional probability

d)

All the above

33.

Which is not the advantages of Vector Model

a)

Term weighting improves quality of the answer set

b)

Cosine ranking formula sorts documents according to a degree of similarity to the query

c)

Assumes independence of index terms

d)

Partial matching allows retrieval of docs that approximate the query conditions

34.

LSI is an indexing and retrieval method that uses a mathematical technique called

____

a)

linear programming

,

b)

non linear programming

c)

Lagrangian Relaxation

d)

singular value decomposition

35.

LSI Solves the problem of_____

a)

Synonymy

b)

Polysemy

c)

A & B

d)

NONE

36.

A term conjunctive component that

satisfies a query is called as _______-

a)

Query Conjunctive Component

b)

Disjoint Normal form

c)

Boolean Conjunctive Component

d)

Term Frequency Conjunctive Component

37.

____

model Computes the similarity between documents and query , by Assigning non-binary weights to index terms in queries and in documents

a)

Boolean model

b)

Vector space Model

c)

probabilistic model

d)

All the above

38.

______is the fraction of the retrieved documents (the set A) which is relevant

a)

Precision

b)

Recall

c)

Term Frequency

d)

document frequency

39.

_____is the fraction of the relevant documents

(the set R)which has been retrieved

a)

Recall

b)

Precision

c)

document

frequency

d)

Term

Frequency

40.

Term weights are used to compute a

_______between a query and each document

a)

Degree of Similarity

b)

Degree of dissimilarity

c)

Inverse document frequency

d)

Term frequency

IRT ASSESSMENT 1

Total questions: 40

Worksheet time: 20mins

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