'Document collection' presentation slideshows

Question Answering: Overview of Tasks and Approaches

Question Answering: Overview of Tasks and Approaches. Horacio Saggion Department of Computer Science University of Sheffield England, United Kingdom http://www.dcs.shef.ac.uk/~saggion. QA Task QA in TREC QA Architecture Collection Indexing Question Analysis Document Retrieval

983 views • 77 slides

15-853:Algorithms in the Real World

15-853:Algorithms in the Real World. Indexing and Searching (how google and the likes work). Indexing and Searching Outline. Introduction: model, query types Inverted File Indices: Compression, Lexicon, Merging Link Analysis : PageRank (Google), Hubs & Authorities Duplicate Removal.

726 views • 63 slides

Introduction to Information Retrieval

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Information Retrieval: Indexing

Information Retrieval: Indexing. Acknowledgements: Dr Mounia Lalmas (QMW) Dr Joemon Jose (Glasgow). Roadmap. What is a document? Representing the content of documents Luhn's analysis Generation of document representatives Weighting Inverted files. Indexing Language.

497 views • 47 slides

15-499:Algorithms and Applications

15-499:Algorithms and Applications. Indexing and Searching I (how google and the likes work). Indexing and Searching Outline. Introduction: model, query types Inverted Indices: Compression, Lexicon, Merging Vector Models: Latent Semantic Indexing: Link Analysis : PageRank (Google), HITS

615 views • 45 slides

Probabilistic Information Retrieval Part I: Survey

Probabilistic Information Retrieval Part I: Survey. Alexander Dekhtyar department of Computer Science University of Maryland. Outline. Part I: Survey : Why use probabilities ? Where to use probabilities ? How to use probabilities ? Part II: In Depth : Probability Ranking Principle

787 views • 42 slides

CS276A Text Information Retrieval, Mining, and Exploitation

CS276A Text Information Retrieval, Mining, and Exploitation. Lecture 4 15 Oct 2002. Recap of last time. Index size Index construction techniques Dynamic indices Real world considerations. Back of the envelope index size calculation. Number of docs = n = 40M Number of terms = m = 1M

534 views • 39 slides

Distributional models

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Transductive Inference for Text Classification using Support Vector Machines

Transductive Inference for Text Classification using Support Vector Machines. Thorsten Joachims Universit at Dortmund, LS VIII 44221 Dortmund, Germany joachims@ls8.cs.uni-dortmund.de. Presented by Yueng-Tien ,Lo. outline. Introduction Text Classification

712 views • 34 slides

CSC 9010: Text Mining Applications Document-Level Techniques

CSC 9010: Text Mining Applications Document-Level Techniques. Dr. Paula Matuszek Paula_A_Matuszek@glaxosmithkline.com (610) 270-6851. Dealing with Documents. Sometimes our information need is not for something specific which we can capture in a clearcut knowledge model

575 views • 34 slides

Introduction to Text Retrieval

Introduction to Text Retrieval. CSE3201/4500 Information Retrieval Systems. Database Types. highly-structured. Relational DB. XML collections. Text Collections. Multimedia Collections. ill-structured. Ill-structured data. Attributes: Variable length records, fields

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Information Retrieval: Models and Methods

Information Retrieval: Models and Methods. October 15, 2003 CMSC 35900 Gina-Anne Levow. Roadmap. Problem: Matching Topics and Documents Methods: Classic: Vector Space Model N-grams HMMs Challenge: Beyond literal matching Expansion Strategies Aspect Models.

574 views • 33 slides

NCAA Enforcement: How to Conduct a Campus Investigation

NCAA Enforcement: How to Conduct a Campus Investigation. 2014 NCAA Regional Rules Seminars. Session Overview. Purpose:

383 views • 30 slides

Text Mining

465 views • 26 slides

Automatic Indexing (Term Selection)

Automatic Indexing (Term Selection). Automatic Text Processing by G. Salton, Chap 9, Addison-Wesley, 1989. Automatic Indexing. Indexing: assign identifiers (index terms) to text documents. Identifiers: single-term vs. term phrase

464 views • 26 slides

Indexing by Latent Semantic Analysis

Indexing by Latent Semantic Analysis. Written by Deerwester, Dumais, Furnas, Landauer, and Harshman (1990) Reviewed by Cinthia Levy. Latent Semantic Indexing. Term-matching Most retrieval systems match words of a query (keywords) with words of a document. Problem

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Space-Efficient Algorithms for Document Retrieval

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National Nursing Assessment Implementation Project

418 views • 16 slides

QUALITATIVE AND QUANTATIVE

QUALITATIVE AND QUANTATIVE. COMPARED Gay, L.R. 1996. Educational Research: Competencies for Analysis and Application. New Jersey: Prentice Hall. Qualitative Explain, gain insight and understanding of, phenomena through intensive collection of narrative data. Quantitative

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