Information Retrieval and Web Search Lecture 1. Course overview Instructor: Rada Mihalcea Class web page:

Slides:



Advertisements
Similar presentations
Modern Information Retrieval Chapter 1: Introduction
Advertisements

An Introduction to Information Retrieval and Applications J. H. Wang Feb. 19, 2008.
ARNOLD SMEULDERS MARCEL WORRING SIMONE SANTINI AMARNATH GUPTA RAMESH JAIN PRESENTERS FATIH CAKIR MELIHCAN TURK Content-Based Image Retrieval at the End.
Web- and Multimedia-based Information Systems. Assessment Presentation Programming Assignment.
ISP 433/533 Week 2 IR Models.
Basic IR: Queries Query is statement of user’s information need. Index is designed to map queries to likely to be relevant documents. Query type, content,
Modern Information Retrieval Chapter 1: Introduction
CS/CMPE 535 – Machine Learning Outline. CS Machine Learning (Wi ) - Asim LUMS2 Description A course on the fundamentals of machine.
Fall 2004 WWW IS112 Prof. Dwyer Intro1: Overview and Syllabus Professor Catherine Dwyer.
SLIDE 1IS 202 – FALL 2004 Lecture 13: Midterm Review Prof. Ray Larson & Prof. Marc Davis UC Berkeley SIMS Tuesday and Thursday 10:30 am -
Overview Discrete Mathematics and Its Applications Baojian Hua
An introduction to databases In this module, you will learn: What exactly a database is How a database differs from an internet search engine How to find.
Chapter 5 Application Software.
CS598CXZ Course Summary ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign.
Syllabus CS 765: Introduction to Database Management Systems Fall 2008 Text Database Management Systems Ramakrishnan/Gehrke, 3rd.
© Paradigm Publishing, Inc. 5-1 Chapter 5 Application Software Chapter 5 Application Software.
CS223 Algorithms D-Term 2013 Instructor: Mohamed Eltabakh WPI, CS Introduction Slide 1.
Cpt S 471/571: Computational Genomics Spring 2015, 3 cr. Where: Sloan 9 When: M WF 11:10-12:00 Instructor weekly office hour for Spring 2015: Tuesdays.
CS621 : Seminar-2008 DEEP WEB Shubhangi Agrawal ( )‏ Jayalekshmy S. Nair ( )‏
Welcome to CS 3260 Dennis A. Fairclough. Overview Course Canvas Web Site Course Materials Lab Assignments Homework Grading Exams Withdrawing from Class.
COMP Introduction to Programming Yi Hong May 13, 2015.
CS523 INFORMATION RETRIEVAL COURSE INTRODUCTION YÜCEL SAYGIN SABANCI UNIVERSITY.
Computer Networks CEN 5501C Spring, 2008 Ye Xia (Pronounced as “Yeh Siah”)
Information Retrieval CENG 555 Spring Course Web Page Authoritative source of administrivia In-class announcements generally reflected on Web.
1 Searching through the Internet Dr. Eslam Al Maghayreh Computer Science Department Yarmouk University.
20-753: Fundamentals of Web Programming 1 Lecture 1: Introduction Fundamentals of Web Programming Lecture 1: Introduction.
Homework 4 Final homework Deadline: Sunday April 20, PM In this homework you have to write a short essay on how Google can handle new types of data.
Course Overview for Web Computing J. H. Wang Sep. 19, 2011.
Web Searching Basics Dr. Dania Bilal IS 530 Fall 2009.
Search Engine By Bhupendra Ratha, Lecturer School of Library and Information Science Devi Ahilya University, Indore
Thanks to Bill Arms, Marti Hearst Documents. Last time Size of information –Continues to grow IR an old field, goes back to the ‘40s IR iterative process.
1 Information Retrieval Acknowledgements: Dr Mounia Lalmas (QMW) Dr Joemon Jose (Glasgow)
Xiaoying Gao Computer Science Victoria University of Wellington Intelligent Agents COMP 423.
Autumn Web Information retrieval (Web IR) Handout #0: Introduction Ali Mohammad Zareh Bidoki ECE Department, Yazd University
Overviews of ITCS 6161/8161: Advanced Topics on Database Systems Dr. Jianping Fan Department of Computer Science UNC-Charlotte
B. Prabhakaran1 Multimedia Systems Textbook Any/Most Multimedia Related Books Reference Papers: Appropriate reference papers discussed in class from time.
Basic Research Skills Created by Deana Hueners For DSU Composition Students.
© Paradigm Publishing Inc. 5-1 Chapter 5 Application Software.
Course grading Project: 75% Broken into several incremental deliverables Paper appraisal/evaluation/project tool evaluation in earlier May: 25%
Introduction to Operating Systems J. H. Wang Sep. 15, 2010.
Course Overview: An Introduction to Information Retrieval and Applications J. H. Wang Feb. 22, 2012.
Modern Information Retrieval Presented by Miss Prattana Chanpolto Faculty of Information Technology.
Introduction to Information Retrieval Example of information need in the context of the world wide web: “Find all documents containing information on computer.
Information Retrieval CSE 8337 Spring 2007 Introduction/Overview Some Material for these slides obtained from: Modern Information Retrieval by Ricardo.
Information Retrieval and Web Search Course overview Instructor: Rada Mihalcea.
Information Retrieval
Digital Video Library Network Supervisor: Prof. Michael Lyu Student: Ma Chak Kei, Jacky.
C Programming Lecture 1 : Introduction Bong-Soo Sohn Associate Professor School of Computer Science and Engineering Chung-Ang University.
CS151 Introduction to Digital Design Noura Alhakbani Prince Sultan University, College for Women.
Introduction to Operating Systems J. H. Wang Sep. 13, 2013.
1 Intro to Artificial Intelligence COURSE # CSC384H1F Fall 2008 Sonya Allin Note: many slides drawn from/inspired by Andrew Moore’s lectures at CMU and.
CS798: Information Retrieval Charlie Clarke Information retrieval is concerned with representing, searching, and manipulating.
CS210: Programming Languages Overview of class Dr. Robert Heckendorn.
Lecture 1 Page 1 CS 236 Online Introduction CS 236 On-Line MS Program Networks and Systems Security Peter Reiher.
Information Retrieval CIS-462 Dr. Samir Tartir 2013/2014 First Semester.
Course Information CSE 2031 Fall Instructor U. T. Nguyen /new-yen/ Office: CSEB Office hours:  Tuesday,
CSE6339 DATA MANAGEMENT AND ANALYSIS FOR COMPUTATIONAL JOURNALISM CSE6339, Spring 2012 Department of Computer Science and Engineering, University of Texas.
Welcome to CS 4390/CS5381: Introduction to Formal Methods
Course Overview - Database Systems
Information Storage and Retrieval Fall Lecture 1: Introduction and History.
CS101 Computer Programming I
Proposal for Term Project
What is IR? In the 70’s and 80’s, much of the research focused on document retrieval In 90’s TREC reinforced the view that IR = document retrieval Document.
Searching for and Accessing Information
Thanks to Bill Arms, Marti Hearst
EE422C Software Design and Implementation II
Multimedia Information Retrieval
Introduction to Information Retrieval
Information Retrieval CIS-462
ADVANCED TOPICS IN INFORMATION RETRIEVAL AND WEB SEARCH
Presentation transcript:

Information Retrieval and Web Search Lecture 1. Course overview Instructor: Rada Mihalcea Class web page:

Slide 1 What is this course about? Processing Indexing Retrieving … textual data Fits in four lines, but much more complex and interesting than that

Slide 2 Need for IR With the advance of WWW - more than 3 Billion documents indexed on Google Various needs for information: –Search for documents that fall in a given topic –Search for a specific information –Search an answer to a question –Search for information in a different language

Slide 3 Some definitions of Information Retrieval (IR) Salton (1989): “Information-retrieval systems process files of records and requests for information, and identify and retrieve from the files certain records in response to the information requests. The retrieval of particular records depends on the similarity between the records and the queries, which in turn is measured by comparing the values of certain attributes to records and information requests.” Kowalski (1997): “An Information Retrieval System is a system that is capable of storage, retrieval, and maintenance of information. Information in this context can be composed of text (including numeric and date data), images, audio, video, and other multi-media objects).”

Slide 4 Examples of IR systems Conventional (library catalog) Search by keyword, title, author, etc. E.g. : You are probably familiar with Text-based (Lexis-Nexis, Google, FAST). Search by keywords. Limited search using queries in natural language. Multimedia (QBIC, WebSeek, SaFe) Search by visual appearance (shapes, colors,… ). Question answering systems (AskJeeves, Answerbus) Search in (restricted) natural language Other: cross language information retrieval, music retrieval

Slide 5

Slide 6

Slide 7 IR systems on the Web Search for Web pages Search for images Search for image content Search for answers to questions Search for music?

Slide 8 Course information Instructor: Rada Mihalcea Contact info: NTRP 228, , Teaching assistant: TBA Class meets TTh, 2:00-3:20pm Office hourse –T, 4:00-5:30pm –Any time electronically –For grading, programming problems, first try to get in touch with the TA.

Slide 9 Course resources Textbook: –Modern Information Retrieval Ricardo Baeza-Yates and Berthier Ribeiro-Neto Recommended: –Readings in Information Retrieval K.Sparck Jones and P. Willett –See the class website for pointers to places to buy them for less Papers from conferences, journals will be assigned throughout the course. Whenever possible, a copy of the paper will be placed on the class website.

Slide 10 Grading Homeworks: 30% –Start early! Some may be time consuming –3 days late policy Midterm I: 15% Midterm II: 15% Project: 30% Class participation: 10% Good news! No final – final is replaced by the project

Slide 11 Programming language Students are free to choose the programming language they want to work with However: –I recommend working with Perl –We’ll have a short Perl tutorial next 1-2 lectures –Why Perl? Makes life much much more easier for text processing problems and for Web based applications Information Retrieval involves a lot of text processing, and often involves Web access –Code reusability Regardless of the language, code MUST compile and run on the CSP Linux machines. –No credit will be given for programs that do not compile!

Slide 12 Tentative schedule Course Overview Short Perl Tutorial Introduction to IR models and methods Text analysis / document preprocessing Vectorial model Boolean model Probabilistic model; other IR models IR collections IR evaluation Query operations Query languages Natural Language IR (Named Entity recognition)

Slide 13 Tentative schedule Natural Language IR (Semantic ambiguity, conceptual indexing) Natural Language IR (Phrase indexing, other) Question Answering: TREC / Web Information extraction Text classification/Topic tracking and detection Web IR: crawlers Web IR: search engines Web IR: link based / content based Web IR: evaluation metrics / Midterm review Special topics: Cross Language IR Special topics Final IR overview, future directions …. Midterm I, Midterm II, Project presentations