'Inverted files' presentation slideshows

Lecture 5: Boolean and Extended Boolean

Prof. Ray Larson University of California, Berkeley School of Information. Lecture 5: Boolean and Extended Boolean. Principles of Information Retrieval. Today. Review IR Components Inverted Files IR Models The Boolean Model Fuzzy sets, Rubric, P-norm, etc. Structure of an IR System.

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Modern information retrieval

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Information Retrieval

Information Retrieval. CSE 8337 Spring 2005 Indexing and Searching Material for these slides obtained from: Modern Information Retrieval by Ricardo Baeza-Yates and Berthier Ribeiro-Neto http://www.sims.berkeley.edu/~hearst/irbook/ Prof. Mario Nascimento of University of Alberta.

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Building a Distributed Full-Text Index for the Web

Building a Distributed Full-Text Index for the Web. by Sergey Melnik, Sriram Raghavan, Beberly Yang and Garcia-Molina. Why do we care.

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CS336 Lecture 5:

CS336 Lecture 5:. Inverted Files, Signature Files, Bitmaps. Generating Document Representations. Use significant terms to build representations of documents referred to as indexing Manual indexing : professional indexers Assign terms from a controlled vocabulary Typically phrases

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Alexander Gelbukh Gelbukh

Special Topics in Computer Science The Art of Information Retrieval Chapter 8: Indexing and Searching. Alexander Gelbukh www.Gelbukh.com. Previous Chapter: Conclusions. Text transformation: meaning instead of strings Lexical analysis Stopwords Stemming POS, WSD, syntax, semantics

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Alexander Gelbukh Gelbukh

Special Topics in Computer Science The Art of Information Retrieval Chapter 8: Indexing and Searching. Alexander Gelbukh www.Gelbukh.com. Previous Chapter: Conclusions. Text transformation: meaning instead of strings Lexical analysis Stopwords Stemming POS, WSD, syntax, semantics

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Indexing

Indexing. Overview of the Talk. Inverted File Indexing Compression of inverted files Signature files and bitmaps Comparison of indexing methods Conclusion. Usual granularity is document-level, unless a significant fraction of the queries are expected to be proximity-based.

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Alexander Gelbukh Gelbukh

Special Topics in Computer Science Advanced Topics in Information Retrieval Lecture 4 (book chapter 8) : Indexing and Searching. Alexander Gelbukh www.Gelbukh.com. Previous Chapter: Conclusions. Main measures: Precision & Recall. For sets Rankings are evaluated through initial subsets

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Alexander Gelbukh Gelbukh

Special Topics in Computer Science Advanced Topics in Information Retrieval Lecture 7 (book chapter 9) : Parallel and Distributed IR. Alexander Gelbukh www.Gelbukh.com. Previous Chapter: Conclusions. How to accelerate search? Same results as sequential Ideas:

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CS 430: Information Discovery

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CSE 454 - Case Studies

CSE 454 - Case Studies. Design of Alta Vista. Based on a talk by Mike Burrows. AltaVista: Inverted Files. Map each word to list of locations where it occurs Words = null-terminated byte strings Locations = 64 bit unsigned ints Layer above gives interpretation for location URL

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Digital Library Technologies

Digital Library Technologies. Text: formats and storage Searching text Images Speech Multimedia Networking. Text formats. Ascii: simple, no formatting, accessible HTML: simple, moderate formatting, accessible word processors: formatting, access limited

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Presented By Guan Guan

Building a Distributed Full-Text Index for the Web by Sergey Melnik, Sriram Raghavan, Beverly Yang and Hector Garcia-Molina from Stanford University. Presented By Guan Guan. Overview. INTRODUCTION TESTBED ARCHITECTURE PIPELINED INDEXER DESIGN

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Lecture 4: Indexing Files

Lecture 4: Indexing Files. Inverted File Lexical Analysis Stop lists. Indexing. Arrangement of data (data structure) to permit fast searching Which list is easier to search? sow fox pig eel yak hen ant cat dog hog ant cat dog eel fox hen hog pig sow yak. Creating inverted files.

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IR Homework #2

IR Homework #2. By J. H. Wang Mar. 25, 2008. Programming Exercise #2: Term Weighting. Goal: to assign TF-IDF weights for each index term in inverted files Input : inverted index files (the output of HW#1) Output : term weighting files (exact format to be described later).

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