Information Extraction and Retrieval - IIIT Hyderabad

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Probabilistic Model Objective: to capture the IR problem using a probabilistic framework Given a user query, there is an ideal answer set Guess at the beginning what they could be (i.e., guess initial description of ideal answer set) Improve by iteration 28
Probabilistic Model • An initial set of documents is retrieved somehow – Can be done using vectorial model, boolean model • User inspects these docs looking for the relevant ones (in truth, only top 10-20 need to be inspected) • IR system uses this information to refine description of ideal answer set • By repeting this process, it is expected that the description of the ideal answer set will improve • Have always in mind the need to guess at the very beginning the description of the ideal answer set • Description of ideal answer set is modeled in probabilistic terms 29
Page 1 and 2: IR Systems (Note: Many slides from
Page 3 and 4: Approaches to IR Two types of
Page 5 and 6: Typical IR Task Given: Find: A
Page 7 and 8: Web Search System Web Spider Docume
Page 9 and 10: Keyword Search Simplest notion of
Page 11 and 12: Relevant items are similar Much of
Page 13 and 14: How effective is IR? Anecdotal e
Page 15 and 16: Intelligent IR Taking into account
Page 17 and 18: IR System Components Text Operati
Page 19 and 20: IR Models Set Theoretic Classic Mod
Page 21 and 22: Classic IR Models - Basic Concepts
Page 23 and 24: Drawbacks of the Boolean Model •
Page 25 and 26: Vector-based Model • Define: - w
Page 27: Vector Based Model • Sim(q,d j )
Page 31: 31 Thank You - Questions? Vasudeva

Information Extraction and Retrieval - IIIT Hyderabad

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