'Document length' diaporamas de présentation

Cheshire II: Features and Internals and Cheshire III overview

Cheshire II: Features and Internals and Cheshire III overview. Ray R. Larson School of Information Management and Systems University of California, Berkeley. Overview. Cheshire II feature overview Logistic Regression Ranking, Okapi BM-25 and Boolean Operations Fusion Operators

1.32k views • 132 slides

Lemur Toolkit Introduction

Lemur Toolkit Introduction. http://net.pku.edu.cn/~wbia ?? pb@net.pku.edu.cn ???????????? 3/21/2011. Recap. Information Retrieval Models Vector Space Model Probabilistic models Language model. t 3. D. Q. ?. t 1. t 2. Some formulas for Sim (VSM). Dot product Cosine Dice

1.17k views • 99 slides

Ranked Retrieval

Ranked Retrieval. LBSC 796/INFM 718R Session 3 September 24, 2007. Agenda. Ranked retrieval Similarity-based ranking Probability-based ranking. The Perfect Query Paradox. Every information need has a perfect result set All the relevant documents, no others

748 views • 74 slides

Ranked Retrieval

Ranked Retrieval. LBSC 796/INFM 718R Session 3 February 16, 2011. Agenda. Ranked retrieval Similarity-based ranking Probability-based ranking. The Perfect Query Paradox. Every information need has a perfect result set All the relevant documents, no others

757 views • 62 slides

Ranked Retrieval

Ranked Retrieval. LBSC 796/INFM 718R Session 3 February 16, 2011. Agenda. Ranked retrieval Similarity-based ranking Probability-based ranking. The Perfect Query Paradox. Every information need has a perfect result set All the relevant documents, no others

628 views • 62 slides

An Overview of the Indri Search Engine

An Overview of the Indri Search Engine. Don Metzler Center for Intelligent Information Retrieval University of Massachusetts, Amherst. Joint work with Trevor Strohman, Howard Turtle, and Bruce Croft. Outline. Overview Retrieval Model System Architecture Evaluation Conclusions. Zoology 101.

1.12k views • 61 slides

An overview of the technology used Information Retrieval

An overview of the technology used Information Retrieval. Louise Guthrie University of Sheffield. What is Information Retrieval(IR)?. Retrieval of unstructured data Most often - Retrieval of Text Retrieval of Videos Retrieval of Images. Retrieval of Text Documents.

772 views • 55 slides

Full-Text Indexing

858 views • 38 slides

Boolean Model

Boolean Model. Hongning Wang CS@UVa. Feedback. Evaluation. Abstraction of search engine architecture. Indexed corpus. Crawler. Ranking procedure. Doc Analyzer. (Query). User. Query Rep. Doc Representation. Ranker. Indexer. results. Index. Search with Boolean query.

604 views • 35 slides

N -Gram and Local Context Analysis for Persian text retrieval

Tehran University. N -Gram and Local Context Analysis for Persian text retrieval. Abolfazl AleAhmad, Parsia Hakimian, Farzad Mahdikhani School of Electrical and Computer Engineering University of Tehran Farhad Oroumchian University of Wollongong in Dubai. Outline. The Persian Language

452 views • 33 slides

Search Engines WS 2009 / 2010

Search Engines WS 2009 / 2010. Lecture 3, Thursday November 5 th , 2009 (Ranking, Tf.idf, BM25, Precision, Recall, Top-K). Prof. Dr. Hannah Bast Chair of Algorithms and Data Structures Department of Computer Science University of Freiburg. Why Ranking. Very simple answer:

428 views • 32 slides

Effective Keyword Search in Relational Databases

Effective Keyword Search in Relational Databases. Fang Liu (University of Illinois at Chicago) Clement Yu (University of Illinois at Chicago) Weiyi Meng (Binghamton University) Abdur Chowdhury (America Online, Inc.). Effective Keyword Search in Relational Databases. Introduction

324 views • 32 slides

Lower-Bounding Term Frequency Normalization

Lower-Bounding Term Frequency Normalization. Yuanhua Lv and ChengXiang Zhai University of Illinois at Urbana-Champaign CIKM 2011 Best Student Award Paper Speaker: Tom Nov 8 th , 2011. It is very difficult to improve retrieval models. BM25 [Robertson et al. 1994]

501 views • 31 slides

Statistical Methods

476 views • 30 slides

EE3J2 Data Mining Lecture 8 Latent Semantic Indexing Martin Russell

EE3J2 Data Mining Lecture 8 Latent Semantic Indexing Martin Russell. Objectives. To explain vector representation of documents To understand cosine distance To understand, intuitively, Latent Semantic Indexing (LSI) To understand, intuitively, how to do Latent Semantic Analysis (LSA).

416 views • 19 slides

Seizures

400 views • 13 slides

ARISTOTLE UNIVERSITY OF THESSALONIKI

ARISTOTLE UNIVERSITY OF THESSALONIKI. Baseline Document Retrieval Component N. Bassiou, C. Kotropoulos, I. Pitas. 20/07/2000, Page 1. ARISTOTLE UNIVERSITY OF THESSALONIKI. Introduction

268 views • 12 slides

Audience, Purpose, & Context

Audience, Purpose, & Context. Who? Why? What?. Analyzing the Audience . Who will be reading, listening to, or using this material? What particular characteristics do they have? What are their backgrounds and attitudes towards the subject?. Typical Business Audiences. Subordinates

138 views • 11 slides

Seizures

670 views • 10 slides

Information Retrieval and Organisation

Information Retrieval and Organisation. Chapter 11 Probabilistic Information Retrieval. Dell Zhang Birkbeck, University of London. Why P robabilities in IR?. Query Representation. User Information Need. Understanding of user need is uncertain. How to match?. Uncertain guess of

209 views • 9 slides