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Information retrieval multiple choice questions with answers, IR MCQs with answers, solved information retrieval questions, information retrieval objective type questions with solutions, information retrieval exam questions

Information Retrieval Multiple Choice Questions with Answers

• Information Retrieval MCQs with Answers - SET 01

  • Keywords: precision, query expansion, classical IR models
    

• Information Retrieval MCQs with Answers - SET 02

  • Keywords: stop word removal, heaps' law, inverted index, idf
    

• Information Retrieval MCQs with Answers - SET 03

  • Keywords: Zipf's law, inverted index, IR formal definition, fallout ratio
    

• Information Retrieval MCQs with Answers - SET 04

  • Keywords: Stemming, query expansion, recall, relevance, homonym
    

• Information Retrieval MCQs with Answers - SET 05

  • Keywords: Stemming, recall, click relevance feedback, TF-IDF
    

• Information Retrieval MCQs with Answers - SET 06

• Keywords: Precision, false positive

• Information Retrieval MCQs with Answers - SET 07

• Information Retrieval MCQs with Answers - SET 08

• Information Retrieval MCQs with Answers - SET 09

• Information Retrieval MCQs with Answers - SET 10

• Information Retrieval MCQs with Answers - SET 11

• Information Retrieval MCQs with Answers - SET 12

• Information Retrieval MCQs with Answers - SET 13

• Information Retrieval MCQs with Answers - SET 14

• Information Retrieval MCQs with Answers - SET 15

 

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List down the local methods for improving recall of IR systems

What are the local methods to improve recall in a retrieval system, relevance feedback, pseudo relevance feedback methods to improve recall, local methods for query reformulation in IR system

 

Question: List down the local methods for improving recall of IR systems   Answer: Local methods go for local on-demand analysis for a user query in order to improve recall of a retrieval system. Following are the local methods for improving recall; 1. Relevance feedback It improves the query based on user feedback on relevance of the documents in the initial set of results. Users were asked explicitly to mark the documents as relevant or non-relevant. This feedback is used to reformulate the query to produce new search results. 2. Pseudo relevance feedback Pseudo relevance feedback, also known as blind relevance feedback, provides a method for automatic local analysis. It automates the manual part of relevance feedback, so that the user gets improved retrieval performance without an extended interaction. 3. Indirect relevance feedback

 

Related links/questions

• How to improve the recall of an information retrieval system

• What are the global resources for query expansion / reformulation?

• Machine learning multiple choices questions with detailed answers - home page

• What are the problems/disadvantages with Jaccard similarity coefficient?
  

• What are the components of a benchmark data collection?

• What are the drawbacks of Boolean retrieval model?

• What are the drawbacks of relevance feedback in improving search results

• Common preprocessing steps and their significance in information retrieval

 

 

             

Keywords

Relevance feedback in information retrieval

Pseudo relevance feedback in information retrieval

Evaluation of information retrieval systems

Local methods for query reformulation

IR techniques for relevance feedback

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Query expansion techniques for information retrieval

List down the common global resources for query expansion

List down the global resources applicable for query expansion, what are the resources useful for query expansion or query reformulation, how does query expansion is achieved? global method for improving recall of an information retrieval system

 

Question: List down the common resources for query reformulation / expansion   Answer: Query expansion is a global method for query reformulation. It is used for adding related words to a query in order to increase the number of returned documents and improve recall accordingly. It often referred as “global query expansion”. As the method uses global resources such as thesaurus and query logs that are not usually query dependent, it is referred to as “global query expansion”. Query expansion uses the following global resources for query reformulation; 1. Controlled vocabulary There is a canonical term for each concept; all other terms for the same concept are replaced by the canonical term 2. Manual thesaurus Human annotators build sets of synonymous terms for concepts. Example: PubMed. 3. Automatically generated thesaurus Attempt to generate a thesaurus automatically by analyzing the distribution of words in documents 4. Query log mining Query logs are maintained by each search engine in order to analyze the behavior of the user while interacting with search engine. These kind of approaches use these query logs to analyze the user’s preference and adds corresponding terms to query.

 

Related links/sources

• How to improve the recall of an information retrieval system

• List down the local methods for improving recall of IR systems

• Machine learning multiple choices questions with detailed answers - home page

• What are the problems/disadvantages with Jaccard similarity coefficient?

• What are the components of a benchmark data collection?

• What are the drawbacks of Boolean retrieval model?

• What are the drawbacks of relevance feedback in improving search results

• Common preprocessing steps and their significance in information retrieval

 

 

 

Keywords

Query expansion techniques

Query expansion methods in information retrieval

Query expansion techniques for question answering

Query expansion to improve document retrieval

Global query expansion how to do

Global methods for improving recall of an IR system

Improve the effectiveness of information retrieval

Global option for improving the recall

What is global query expansion?

Why query expansion is referred to as global method?

 

 

How to improve the recall of an information retrieval system

How to improve the recall of an IR system, what are the different methods available to improve the search recall? State the global method and local method for effective recall.

Question:

How to increase recall in information retrieval?   Answer: Recall is the fraction of relevant documents that are retrieved. By modifying the initial query, we can improve the recall. This can be done as follows; 1. By adding new terms to the original query terms that are semantically related to the terms of the original query. This is referred as global method. 2. By making the query vector more similar to vectors of relevant documents. We need some indication of relevance either provided by the user or assumed by the model. This approach is referred as local method.

 

Related links/questions

 

• What are the global resources for query expansion / reformulation?
  

• List down the local methods for improving recall of IR systems
  

• Machine learning multiple choices questions with detailed answers - home page

• What are the problems/disadvantages with Jaccard similarity coefficient?

• What are the components of a benchmark data collection?

• What are the drawbacks of Boolean retrieval model?

• What are the drawbacks of relevance feedback in improving search results

• Common preprocessing steps and their significance in information retrieval

 

 

 

Keywords

How to improve recall in information retrieval

Recall formula in information retrieval

How to evaluate information retrieval system

How to improve precision and recall

Information retrieval system evaluation

How do you evaluate an IR system

Improving the effectiveness of information retrieval system

Calculate the TF-IDF weight for tems of a given document

Find the TF-IDF of terms of a given document and a collection of documents, how to calculate tf-idf, the use of tf-idf in finding the importance of a term in a document, term frequency-inverse document frequency

Question:

Given a document X containing terms t1, t2 and t3 with frequencies (inside brackets) as follows;

t1(3), t2(2), t3(1)

Let us assume that the collection contains 10,000 documents and document frequencies of these terms are as follows;

t1(50), t2(1300), t3(250)

Then, find the TF-IDF weight of terms t1, t2, and t3 in the document X.

 

Solution:

TF-IDF (Term Frequency-Inverse Document Frequency) is a measure to calculate “how relevant a term is in a given document”.

TF_t,d counts the number of times a term t occurs in a document d. It can be calculated as follows;

For example, if the document D1 contains the term ‘quick’ 10 times, and it has 54 words in it, then the TF_{’quick’, D1} = 10/54 = 0.19.

DF_t refers to the number of documents in which t presents.

For example, if 120 documents consist of the word ‘quick’, then the DF_’quick’ = 120.

IDF_t is the inverse measure used to calculate the informativeness of the given term t. This means, how common or rare a word is in the entire document set. The closer it is to 0, the more common a word is. This can be calculated as follows;

Here, N is the number of documents in the given collection, and DF_t is the document frequency of term t.

The TF-IDF weight of a term is the product of its TF weight and its IDF weight.

TF-IDF for term t1;

TF_t1 = (number of times t1 occurs in X)/(number of words in X) = 3/3

IDF_t1 = log(No. of docs in the collection/No. of docs t1 appears) = log(10000/50) = 5.3

TF-IDF for t1 = 5.3

 

TF-IDF for term t2;

TF_t2 = 2/3

IDF_t2 = log (10000/1300) = 2.0

TF-IDF for t2 = 1.3

 

TF-IDF for term t3;

TF_t3 = 1/3

IDF_t3 = log (10000/250) = 3.7

TF-IDF for t3 = 1.23

 

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