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.

Slides:



Advertisements
Similar presentations
Data Mining and the Web Susan Dumais Microsoft Research KDD97 Panel - Aug 17, 1997.
Advertisements

Metadata in Carrot II Current metadata –TF.IDF for both documents and collections –Full-text index –Metadata are transferred between different nodes Potential.
WEB MINING. Why IR ? Research & Fun
Chapter 5: Introduction to Information Retrieval
0 General information Rate of acceptance 37% Papers from 15 Countries and 5 Geographical Areas –North America 5 –South America 2 –Europe 20 –Asia 2 –Australia.
Search Engines and Information Retrieval
IR Challenges and Language Modeling. IR Achievements Search engines  Meta-search  Cross-lingual search  Factoid question answering  Filtering Statistical.
Intelligent Information Retrieval CS 336 Lisa Ballesteros Spring 2006.
Measuring Semantic Similarity between Words Using Web Search Engines Danushka Bollegala, Yutaka Matsuo, Mitsuru Ishizuka Topic  Semantic similarity measures.
Information Retrieval Concerned with the: Representation of Storage of Organization of, and Access to Information items.
Web Information Retrieval and Extraction Chia-Hui Chang, Associate Professor National Central University, Taiwan Sep. 16, 2005.
Semantic (Language) Models: Robustness, Structure & Beyond Thomas Hofmann Department of Computer Science Brown University Chief Scientist.
PT3 Mentoring & Technology Summer Institute 2002.
Information Retrieval in Practice
AQUAINT Kickoff Meeting – December 2001 Integrating Robust Semantics, Event Detection, Information Fusion, and Summarization for Multimedia Question Answering.
CS598CXZ Course Summary ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign.
Information Retrieval – Introduction and Survey Norbert Fuhr University of Duisburg-Essen Germany
Challenges in Information Retrieval and Language Modeling Michael Shepherd Dalhousie University Halifax, NS Canada.
Search Engines and Information Retrieval Chapter 1.
Dept. Computer Science, Korea Univ. Intelligent Information System Lab. XML clustering methods Sohn Jong-Soo Intelligent Information.
GLOSSARY COMPILATION Alex Kotov (akotov2) Hanna Zhong (hzhong) Hoa Nguyen (hnguyen4) Zhenyu Yang (zyang2)
Text summarization MEAD NewsInEssence Cross-document structure Sentence compression Lexrank Political science Discourse dynamics Centrality identification.
Introduction to Web Mining Spring What is data mining? Data mining is extraction of useful patterns from data sources, e.g., databases, texts, web,
Information Retrieval and Web Search Lecture 1. Course overview Instructor: Rada Mihalcea Class web page:
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.
Search and Information Extraction Lab IIIT Hyderabad.
Research Projects 6v81 Multimedia Database Yohan Jin, T.A.
Text Based Information Retrieval Text Based Information Retrieval H02C8A H02C8B Marie-Francine Moens Karl Gyllstrom Katholieke Universiteit Leuven.
Toward A Session-Based Search Engine Smitha Sriram, Xuehua Shen, ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign.
1 CSI 5180: Topics in AI: Natural Language Processing, A Statistical Approach Instructor: Nathalie Japkowicz Objectives of.
SLIDE 1IS 240 – Spring 2009 Prof. Ray Larson University of California, Berkeley School of Information Principles of Information Retrieval.
Collocations and Information Management Applications Gregor Erbach Saarland University Saarbrücken.
WIRED Week 3 Syllabus Update (next week) Readings Overview - Quick Review of Last Week’s IR Models (if time) - Evaluating IR Systems - Understanding Queries.
Research Topics/Areas. Adapting search to Users Advertising and ad targeting Aggregation of Results Community and Context Aware Search Community-based.
WEB MINING. In recent years the growth of the World Wide Web exceeded all expectations. Today there are several billions of HTML documents, pictures and.
Group A Next Generation Information Access Group.
For Monday Read chapter 26 Last Homework –Chapter 23, exercise 7.
1 Information Retrieval LECTURE 1 : Introduction.
2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, 龙星计划课程 : 信息检索 Course Summary ChengXiang Zhai ( 翟成祥 ) Department of.
Finding document topics for improving topic segmentation Source: ACL2007 Authors: Olivier Ferret (18 route du Panorama, BP6) Reporter:Yong-Xiang Chen.
Automatic Labeling of Multinomial Topic Models
Relevance Models and Answer Granularity for Question Answering W. Bruce Croft and James Allan CIIR University of Massachusetts, Amherst.
CS798: Information Retrieval Charlie Clarke Information retrieval is concerned with representing, searching, and manipulating.
Text Information Management ChengXiang Zhai, Tao Tao, Xuehua Shen, Hui Fang, Azadeh Shakery, Jing Jiang.
Welcome to CPSC 534B: Information Integration Laks V.S. Lakshmanan Rm. 315.
AQUAINT Mid-Year PI Meeting – June 2002 Integrating Robust Semantics, Event Detection, Information Fusion, and Summarization for Multimedia Question Answering.
Trends in NL Analysis Jim Critz University of New York in Prague EurOpen.CZ 12 December 2008.
NSF Grant Number: IIS PI: Joseph Picone Institution: Mississippi State University Title: Integrating Prosody, Speech Recognition, Parsing In Spoken-Language.
Guangbing Yang Presentation for Xerox Docushare Symposium in 2011
Information Retrieval and Web Search
Web Engineering.
Course Summary (Lecture for CS410 Intro Text Info Systems)
Information Retrieval and Web Search
Knowledge Management Systems
Information Retrieval and Web Search
Web IR: Recent Trends; Future of Web Search
Dr. Sudha Ram Huimin Zhao Department of MIS University of Arizona
Thanks to Bill Arms, Marti Hearst
15-826: Multimedia Databases and Data Mining
15-826: Multimedia Databases and Data Mining
DBMS with probabilistic model
Text Categorization Rong Jin.
CSE 635 Multimedia Information Retrieval
Dr. Bhavani Thuraisingham The University of Texas at Dallas
Web Mining Department of Computer Science and Engg.
Introduction to Information Retrieval
Comprehension content Domains
What’s the QUESTION? Do Now!
Information Retrieval and Web Design
Information Retrieval and Web Search
Presentation transcript:

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 retrieval is important e.g. Web search! But researchers work on a range of retrieval-related language technologies: question answering cross-lingual retrieval distributed retrieval topic detection and tracking multimedia retrieval summarization

IR and Database Systems Typically differentiated by unstructured/structured data What about marked-up text and semi-structured data? Recent database papers on nearest-neighbor and similarity search distributed, peer-to-peer search Web search information extraction text data mining Boundaries continue to get fuzzier

IR and Database Systems Many proposals for database/IR integration most recently in XML context, but goes back to the 70s supporting a probabilistic framework is key Integration vs. Cooperation Semantic Web lessons from the IR world semantics or statistics?