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Resource Discovery Strategies

Resource Discovery Strategies

Resource Discovery Strategies. CS 502 – 20030324 Carl Lagoze – Cornell University. Acknowledgements: Luis Gravano Andreas Paepcke Bill Arms. Function versus cost of acceptance. Cost of acceptance. Z39.50. SDLIP/STARTS. Metadata Harvesting. google. Function.

By Leo
(240 views)

CS276: Information Retrieval and Web Search Christopher Manning and Prabhakar Raghavan

CS276: Information Retrieval and Web Search Christopher Manning and Prabhakar Raghavan

CS276: Information Retrieval and Web Search Christopher Manning and Prabhakar Raghavan Lecture 6: Scoring, Term Weighting and the Vector Space Model. Recap of lecture 5. Collection and vocabulary statistics: Heaps’ and Zipf’s laws Dictionary compression for Boolean indexes

By maddox
(114 views)

SCORING, TERM WEIGHTING AND THE VECTOR SPACE MODEL

SCORING, TERM WEIGHTING AND THE VECTOR SPACE MODEL

SCORING, TERM WEIGHTING AND THE VECTOR SPACE MODEL. Recap of lecture 5. Collection and vocabulary statistics: Heaps’ and Zipf’s laws Dictionary compression for Boolean indexes Dictionary string, blocks, front coding Postings compression: Gap encoding, prefix-unique codes

By tallis
(110 views)

Focused Crawler

Focused Crawler

Focused Crawler. Ben Markines Mira Stoilova Fulya Erdinc. Introduction. Based from the paper presented the first week of class Accelerated Focused Crawling through Online Relevance Feedback by Chakrabarti presented by Mark Meiss

By shaunna
(170 views)

http://www.xkcd.com/628/

http://www.xkcd.com/628/

http://www.xkcd.com/628/. Results Summaries Spelling Correction. David Kauchak cs160 Fall 2009 adapted from: http://www.stanford.edu/class/cs276/handouts/ lecture3-tolerantretrieval.ppt http://www.stanford.edu/class/cs276/handouts/lecture8-evaluation.ppt. Administrative. Course feedback.

By ordell
(79 views)

http://www.xkcd.com/628/

http://www.xkcd.com/628/

http://www.xkcd.com/628/. Summaries and Spelling Corection. David Kauchak cs458 Fall 2012 adapted from: http://www.stanford.edu/class/cs276/handouts/ lecture3-tolerantretrieval.ppt http://www.stanford.edu/class/cs276/handouts/lecture8-evaluation.ppt. Administrative. Assignment 2

By elmo
(134 views)

Generating Queries from User-Selected Text

Generating Queries from User-Selected Text

Generating Queries from User-Selected Text. Date : 2013/03/04 Resource : IIiX’12 Advisor : Dr. Jia -Ling Koh Speaker : I- Chih Chiu. Outline. Introduction Approaches Experiments Conclusion. Outline. Introduction Motivation Goal Flow Chart Approaches Experiments Conclusion.

By duncan
(86 views)

Metasearch Mathematics of Knowledge and Search Engines: Tutorials @ IPAM 9/13/2007

Metasearch Mathematics of Knowledge and Search Engines: Tutorials @ IPAM 9/13/2007

Metasearch Mathematics of Knowledge and Search Engines: Tutorials @ IPAM 9/13/2007. Zhenyu (Victor) Liu Software Engineer Google Inc. vicliu@google.com. Roadmap. The problem Database content modeling Database selection Summary. ??? applied mathematics. Metasearch – the problem.

By nau
(103 views)

University of Palestine

University of Palestine

University of Palestine. Topics In CIS - ITBS 3202 Ms. Eman Alajrami 2 nd Semester 2008-2009. Chapter 2– Part2 Information Retrieval Models. Vector Model. Basic Concept. Each document is described by a set of representative keywords called index term.

By conway
(157 views)

Machine Learning in Practice Lecture 12

Machine Learning in Practice Lecture 12

Machine Learning in Practice Lecture 12. Carolyn Penstein Ros é Language Technologies Institute/ Human-Computer Interaction Institute. Plan for the Day. Announcements Assingment 5 handed out – Due next Thur Note: Readings for next two lectures on Blackboard in Readings folder

By hazina
(81 views)

Project 1: Machine Learning Using Neural Networks

Project 1: Machine Learning Using Neural Networks

Project 1: Machine Learning Using Neural Networks. Ver 1.1. Outline. Classification using ANN Learn and classify text documents Estimate several statistics on the dataset. Class 1. Input. Class 2. Class 3. …. Network Structure. CLASSIC3 Dataset. CLASSIC3.

By boaz
(141 views)

A Technique For Text Clustering Using A Partition Based Approach Doan Nguyen

A Technique For Text Clustering Using A Partition Based Approach Doan Nguyen

A Technique For Text Clustering Using A Partition Based Approach Doan Nguyen. Presentation Outline. Knowledge Retrieval Scenarios Challenges for Clustering of Documents Applicability Assumptions Cluster Analysis Steps An Example Conclusion. Knowledge Retrieval Scenarios.

By kimn
(65 views)

Paper By: Yiming Yang, CMU Jan O. Pedersen, Verity, Inc. Presented By: Prerak Sanghvi

Paper By: Yiming Yang, CMU Jan O. Pedersen, Verity, Inc. Presented By: Prerak Sanghvi

A Comparative Study on Feature Selection in Text Categorization (Proc. 14th International Conference on Machine Learning – 1997). Paper By: Yiming Yang, CMU Jan O. Pedersen, Verity, Inc. Presented By: Prerak Sanghvi Computer Science and Engineering Department

By lester
(134 views)

Discussion Class 6

Discussion Class 6

Discussion Class 6. Ranking Algorithms. Discussion Classes. Format: Question Ask a member of the class to answer Provide opportunity for others to comment When answering: Give your name. Make sure that the TA hears it. Stand up Speak clearly so that all the class can hear.

By missy
(137 views)

Multimedia Data Access

Multimedia Data Access

Multimedia Data Access. Access to multimedia information must be quick so that retrieval time is minimal. Data access is based on metadata generated for different media composing a database. Metadata must be stored using appropriate index structures to provide efficient access.

By kira
(123 views)

Understanding GWAS SNPs

Understanding GWAS SNPs

Understanding GWAS SNPs. Xiaole Shirley Liu Stat 115/215. GWAS SNPs. Association <> Causal What ’ s the most likely causal SNP / Gene in LD with the genotyped SNP? Use functional genomics to identify the disease tissue of origin What ’ s the SNP doing in non-coding regions? RSNPs.

By pules
(325 views)

A Machine Learning Approach for Improved BM25 Retrieval

A Machine Learning Approach for Improved BM25 Retrieval

A Machine Learning Approach for Improved BM25 Retrieval. Krysta M. Svore Microsoft Research One Microsoft Way Redmond, WA 98052 ksvore@microsoft.com. Christopher J. C. Burges Microsoft Research One Microsoft Way Redmond, WA 98052 cburges@microsoft.com.

By konane
(168 views)

CES 514 Data Mining March 11, 2010

CES 514 Data Mining March 11, 2010

CES 514 Data Mining March 11, 2010 Lecture 5: scoring, term weighting, vector space model (Ch 6). Model for ranking documents. Ranked retrieval Zones, parameters in querying Scoring documents Term frequency Collection statistics Weighting schemes Vector space scoring.

By azana
(153 views)

Lecture 6: Scoring, Term Weighting and the Vector Space Model

Lecture 6: Scoring, Term Weighting and the Vector Space Model

Lecture 6: Scoring, Term Weighting and the Vector Space Model. Ch. 6. Ranked retrieval. Thus far, our queries have all been Boolean. Documents either match or don’t. Ch. 6. Problem with Boolean search: feast or famine.

By kirra
(155 views)

CSE 538 MRS BOOK – CHAPTER VI Scoring, Term Weighting and the Vector Space Model

CSE 538 MRS BOOK – CHAPTER VI Scoring, Term Weighting and the Vector Space Model

CSE 538 MRS BOOK – CHAPTER VI Scoring, Term Weighting and the Vector Space Model. Recap of lecture 5. Collection and vocabulary statistics: Heaps’ and Zipf’s laws Dictionary compression for Boolean indexes Dictionary string, blocks, front coding

By rumer
(124 views)

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