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Sunday, 20 June 2021

Multiple Choice Questions with Answers in Information Retrieval SET 2

Top 5 quiz questions in IR, Information retrieval quiz, information retrieval mcqs with answers, information retrieval,  stop word removal, inverted index, heaps' law, precision, recall, query expansion, inverse document frequency

Information Retrieval MCQs - SET 02

1. Which of the following is the local method for improving recall of an information retrieval system?

a) Query expansion

b) Relevance feedback

c) Ontology based model

d) None of the above

Answer: (b) Relevance feedback

Local methods adjust a query relative to the documents that initially appear to match the query.

Relevance feedback does a local on-demand analysis on initial query results to improve the recall of the IR system. Initial results can be refined based on the selection of relevant and non-relevant documents by the user from the initial retrieval results.

 

 

2. The process of removing most common words (and, or, the, etc.) by an information retrieval system before indexing is known as

a) Lemmatization

b) Stop word removal

c) Inverted indexing

d) Normalization

Answer: (b) Stop word removal

Stop words are the most common words in a language (articles, prepositions, conjunctions, etc.). These common words are of little value in helping select documents matching a user need. Stop word removal helps in reduced dataset size and improved system performance.

Stop word removal is done using a predefined stop word list.

Stop word elimination used to be standard in older IR systems. But you need stop words for phrase queries, e.g. “Queen of England”.

 

3. An inverted index arranges data in a sorted order as per

a) the documents

b) the frequency of each document

c) the frequency of each term

d) the terms

Answer: (d) the terms

An inverted index is the sorted list (or index) of keywords (attributes), with each keyword having links to the documents containing that keyword.

Inverted index is a word-oriented mechanism for indexing a text collection to speed up the searching task. The inverted index structure is composed of two elements: the vocabulary (term) and the occurrences. The vocabulary is the set of all different words in the text. For each word in the vocabulary the index stores the documents which contain that word (inverted index).

 

4. The vocabulary size (unique words) of a text can be estimated using

a) Zipf’s law

b) Scientific law

c) Heaps’ law

d) Inverted index rule

Answer: (c) Heaps’ law

Heaps’ law approximates the number of unique words in a text of n words.

The law can be described as the number of words in a document increases, the rate of the count of distinct words available in the document slows down.

The documented definition of Heaps’ law (also called Herdan's law) says that the number of unique words in a text of n words is approximated by

V(n) = K n^β

where K is a positive constant and β is between 0 and 1. K is often upto 100 and β is often between 0.4 and 0.6.

 

 

5. A metric used measure the importance of a term in a text document collection is called

a) Inverse Document Frequency

b) Term Frequency

c) Inverse Term Frequency

d) Document Frequency

Answer: (a) Inverse Document Frequency

Inverse Document Frequency (IDF) is a metric used to measure the importance of a term in a document collection. It is calculated as follows;

idft = log (N/dft)

idf weight indicates the importance of a term based on how common a word in the collection. idf weight for most common words will be lower and rare words will be higher.

IDF affects the ranking of documents for queries with at least two terms.

 

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Related links

 

Keywords

Which metric is used to measure the importance of a term in a collection in IR?

What does Heaps' law do?

How are data in inverted index arranged?

Why do we remove stop words? Importance of removing stop words. Contribution of stop word removal in information retrieval.

How local methods are helpful in improving recall of a retrieval system?

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