Information Extraction and Retrieval - IIIT Hyderabad

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IR System Components Searching retrieves documents that contain a given query token from the inverted index. Ranking scores all retrieved documents according to a relevance metric. User Interface manages interaction with the user: Query input <strong>and</strong> document output. Relevance feedback. Visualization of results. Query Operations transform the query to improve retrieval: Query expansion using a thesaurus. Query transformation using relevance feedback. 18
IR Models Set Theoretic Classic Models Fuzzy Extended Boolean U s e r T a s k <strong>Retrieval</strong>: Adhoc Filtering Browsing boolean vector probabilistic Structured Models Non-Overlapping Lists Proximal Nodes Algebraic Generalized Vector Lat. Semantic Index Neural Networks Probabilistic Inference Network Belief Network Browsing 19 Flat Structure Guided Hypertext
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: IR System Components Text Operati
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 and 28: Vector Based Model • Sim(q,d j )
Page 29 and 30: Probabilistic Model • An initial
Page 31: 31 Thank You - Questions? Vasudeva

Information Extraction and Retrieval - IIIT Hyderabad

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