How to Pick Up Women…. Using IR Strategies By Mike Wooldridge May 9, 2006.

Slides:

Advertisements

Similar presentations

Even More TopX: Relevance Feedback Ralf Schenkel Joint work with Osama Samodi, Martin Theobald.

Advertisements

Information Retrieval (IR) on the Internet. Contents  Definition of IR  Performance Indicators of IR systems  Basics of an IR system  Some IR Techniques.

Multimedia Database Systems

INSTRUCTOR: DR.NICK EVANGELOPOULOS PRESENTED BY: QIUXIA WU CHAPTER 2 Information retrieval DSCI 5240.

UCLA : GSE&IS : Department of Information StudiesJF : 276lec1.ppt : 5/2/2015 : 1 I N F S I N F O R M A T I O N R E T R I E V A L S Y S T E M S Week.

Exercising these ideas  You have a description of each item in a small collection. (30 web sites)  Assume we are looking for information about boxers,

Precision and Recall.

Evaluating Evaluation Measure Stability Authors: Chris Buckley, Ellen M. Voorhees Presenters: Burcu Dal, Esra Akbaş.

Information Retrieval Ling573 NLP Systems and Applications April 26, 2011.

Information Retrieval Review

Evaluation.  Allan, Ballesteros, Croft, and/or Turtle Types of Evaluation Might evaluate several aspects Evaluation generally comparative –System A vs.

Intelligent Information Retrieval CS 336 –Lecture 2: Query Language Xiaoyan Li Spring 2006 Modified from Lisa Ballesteros’s slides.

Modern Information Retrieval

T.Sharon - A.Frank 1 Internet Resources Discovery (IRD) IR Queries.

Information Retrieval Concerned with the: Representation of Storage of Organization of, and Access to Information items.

Modern Information Retrieval Chapter 2 Modeling. Can keywords be used to represent a document or a query? keywords as query and matching as query processing.

A machine learning approach to improve precision for navigational queries in a Web information retrieval system Reiner Kraft

Question Answering using Language Modeling Some workshop-level thoughts.

Modern Information Retrieval Chapter 5 Query Operations.

A Task Oriented Non- Interactive Evaluation Methodology for IR Systems By Jane Reid Alyssa Katz LIS 551 March 30, 2004.

Retrieval Evaluation: Precision and Recall. Introduction Evaluation of implementations in computer science often is in terms of time and space complexity.

Information Retrieval Ch Information retrieval Goal: Finding documents Search engines on the world wide web IR system characters Document collection.

SIMS 202 Information Organization and Retrieval Prof. Marti Hearst and Prof. Ray Larson UC Berkeley SIMS Tues/Thurs 9:30-11:00am Fall 2000.

Evaluating the Performance of IR Sytems

Query Reformulation: User Relevance Feedback. Introduction Difficulty of formulating user queries –Users have insufficient knowledge of the collection.

Lessons Learned from Information Retrieval Chris Buckley Sabir Research

Retrieval Evaluation. Introduction Evaluation of implementations in computer science often is in terms of time and space complexity. With large document.

Learning Bit by Bit Search. Information Retrieval Census Memex Sea of Documents Find those related to “new media” Brute force.

Modern Information Retrieval Chapter 2 Modeling. Can keywords be used to represent a document or a query? keywords as query and matching as query processing.

Important Task in Patents Retrieval Recall is an Important Factor Given Query Patent -> the Task is to Search all Related Patents Patents have Complex.

CS580: Building Web Based Information Systems Roger Alexander & Adele Howe The purpose of the course is to teach theory and practice underlying the construction.

Evaluation.  Allan, Ballesteros, Croft, and/or Turtle Types of Evaluation Might evaluate several aspects Evaluation generally comparative –System A vs.

CS246 Basic Information Retrieval. Today’s Topic  Basic Information Retrieval (IR)  Bag of words assumption  Boolean Model  Inverted index  Vector-space.

LIS618 lecture 11 i/r performance evaluation Thomas Krichel

Evaluation David Kauchak cs458 Fall 2012 adapted from:

Evaluation David Kauchak cs160 Fall 2009 adapted from:

Query Relevance Feedback and Ontologies How to Make Queries Better.

Philosophy of IR Evaluation Ellen Voorhees. NIST Evaluation: How well does system meet information need? System evaluation: how good are document rankings?

Modern Information Retrieval: A Brief Overview By Amit Singhal Ranjan Dash.

1 Information Retrieval Acknowledgements: Dr Mounia Lalmas (QMW) Dr Joemon Jose (Glasgow)

Chapter 6: Information Retrieval and Web Search

Information retrieval 1 Boolean retrieval. Information retrieval (IR) is finding material (usually documents) of an unstructured nature (usually text)

Introduction to Digital Libraries hussein suleman uct cs honours 2003.

Lecture 1: Overview of IR Maya Ramanath. Who hasn’t used Google? Why did Google return these results first ? Can we improve on it? Is this a good result.

IR Theory: Relevance Feedback. Relevance Feedback: Example  Initial Results Search Engine2.

 A Case For Interaction: A Study of Interactive Information Retrieval Behavior and Effectiveness By Jürgen Koenemann and Nicholas J. Belkin (1996) John.

How Do We Find Information?. Key Questions  What are we looking for?  How do we find it?  Why is it difficult? “A prudent question is one-half of wisdom”

Measuring How Good Your Search Engine Is. *. Information System Evaluation l Before 1993 evaluations were done using a few small, well-known corpora of.

Conceptual structures in modern information retrieval Claudio Carpineto Fondazione Ugo Bordoni

Lecture 3: Retrieval Evaluation Maya Ramanath. Benchmarking IR Systems Result Quality Data Collection – Ex: Archives of the NYTimes Query set – Provided.

Performance Measurement. 2 Testing Environment.

Information Retrieval Transfer Cycle Dania Bilal IS 530 Fall 2007.

Cs Future Direction : Collaborative Filtering Motivating Observations:  Relevance Feedback is useful, but expensive a)Humans don’t often have time.

The Loquacious ( 愛說話 ) User: A Document-Independent Source of Terms for Query Expansion Diane Kelly et al. University of North Carolina at Chapel Hill.

Information Retrieval Quality of a Search Engine.

1 Random Walks on the Click Graph Nick Craswell and Martin Szummer Microsoft Research Cambridge SIGIR 2007.

Xiaoying Gao Computer Science Victoria University of Wellington COMP307 NLP 4 Information Retrieval.

1 Boolean Model. 2 A document is represented as a set of keywords. Queries are Boolean expressions of keywords, connected by AND, OR, and NOT, including.

SIMS 202, Marti Hearst Final Review Prof. Marti Hearst SIMS 202.

Modern Information Retrieval

IR Theory: Evaluation Methods

موضوع پروژه : بازیابی اطلاعات Information Retrieval

Evaluation of IR Performance

Inf 722 Information Organisation

CS246: Information Retrieval

Information Retrieval for Evidence-based Practice

Retrieval Utilities Relevance feedback Clustering

Information Retrieval and Web Design

Precision and Recall Reminder:

Presentation transcript:

How to Pick Up Women…

Using IR Strategies By Mike Wooldridge May 9, 2006

User with “information” need

Collection of “documents”

Objective: Formulate a query that returns relevant documents

Too Few Results “Anyone want to see the waterbed in my van?”

Too Many Results “I’m Brad Pitt.”

Controlled Vocabulary

Relevance Feedback “Anyone want to see the waterbed in my van?”

Revised Query “Anyone want to see the swimming pool at my mansion?”

Term Weighting “Anyone want to see the 3 swimming pools at my 9-bedroom mansion?”

Cross-Language Retrieval “Can I buy you a drink?”

Cross-Language Retrieval “¿Puedo le compro una bebida?”

Precision vs. Recall vs. Blood-Alcohol Content

Thanks, Ray!