JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD MCA 2018 INFORMATION RETRIEVAL SYSTEMS Previous Question Paper (June/July - 2018 - R15)
Note: This question paper contains two parts A and B. Part A is compulsory which carries 25 marks. Answer all questions in Part A. Part B consists of 5 Units. Answer any one full question from each unit. Each question carries 10 marks and may have a, b, c as sub questions.
PART - A
What are the major steps in inverted index construction?
Answer
The construction of an inverted index involves the following major steps:
- Tokenization: Breaking the input text into individual tokens or terms.
- Linguistic Processing: Applying techniques like normalization, stemming, and stop-word removal to refine the tokens.
- Dictionary Creation: Identifying unique terms and storing them in a sorted dictionary.
- Posting List Generation: Recording the document IDs and positions where each term appears.
- Merging: Combining partial indices into a final, sorted inverted index structure for efficient retrieval.
Write a short note on term frequency and inverse document frequency.
Answer
Term Frequency (TF) measures how frequently a term occurs in a specific document, reflecting the importance of the term within that document. It is typically calculated as the raw count or a normalized frequency.
Inverse Document Frequency (IDF) measures how important a term is across the entire collection. It is calculated as the logarithm of the total number of documents divided by the number of documents containing the term.
Together, TF-IDF is used to weight terms, effectively penalizing common words that appear in many documents and highlighting terms that are discriminative for specific documents.
Give an example to show the tree representation of XML documents and queries.
Answer
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What is the minimum number of support vectors required for a data set? Explain.
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State and explain the concept of web graph.
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PART - B
How do we process a query using an inverted index and the basic Boolean retrieval model? Explain with example.
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Explain various tasks involved in determining the vocabulary of terms.
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State and explain the basic algorithm for computing vector space scores.
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Describe how to measure effectiveness of adhoc information retrieval systems.
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Write an algorithm for scoring documents with SimNoMERGE.
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Explain in detail about the query likelihood models in information retrieval.
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List and explain the issues involved in the classification of text documents.
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Give the applications of clustering in information retrieval system.
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With an example explain the concept of near duplicates and shingling’s.
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With a neat diagram explain the basic architecture of a crawler.
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