Q.1
14 MarksExplain text classification using k-nearest neighbor classification approach.
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Explain text classification using k-nearest neighbor classification approach.
How to compute tf-idf weight for a specific term and document? Explain.
What is precision and recall? An IR system returns 23 relevant documents, and 17 nonrelevant documents. There are a total of 35 relevant documents in the collection. What is the precision and recall of the system on this search?
Discuss about user relevance feedback. Illustrate the cases where use of only relevance feedback is not enough?
Explain kappa statistic. How is it interpreted? Consider agreement details of two judges on document relevance. Compute kappa statistic for the given details: Judge Relevant Non-Relevant Judge 1 Relevant 250 20 Non-relevant 15 45
What is text classification? List out examples of text classification in the context of information retrieval.
Illustrate cross language information retrieval and the approaches used for it.
Which are the basic tasks in topic detection and tracking? What is story in this context?
Questions repeat across sessions. Working through two or three papers shows you which topics GTU asks most.
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Official Gujarat Technological University (GTU) examination paper for Information Retrieval (IR) (Winter 2025, B.E. · Computer Engineering, Sem 7). Features complete 70-mark regular & remedial examination pattern, official marking distribution across all 5 questions, and direct 1-click official PDF download.
Transcribed for student exam preparation from Gujarat Technological University official examination archives. Questions, syllabus guidelines, and curriculum marking schemes remain the intellectual property of Gujarat Technological University.
3170718 Information Retrieval
Official GTU PDF · Winter 2025
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