2010 © University of Michigan 1 Text Retrieval and Data Mining in SI - An Introduction Qiaozhu Mei School of Information Computer Science and Engineering.

Slides:

Advertisements

Similar presentations

SEARCHING THE BLOGOSPHERE

Advertisements

1 A Probabilistic Approach to Spatiotemporal Theme Pattern Mining on Weblogs Qiaozhu Mei, Chao Liu, Hang Su, and ChengXiang Zhai : University of Illinois.

WEB MINING. Why IR ？ Research & Fun

1 Opinion Integration and Summarization ChengXiang (“Cheng”) Zhai Department of Computer Science Graduate School of Library & Information Science Institute.

INTRODUCTION TO MACHINE LEARNING David Kauchak CS 451 – Fall 2013.

6 Biggest Mistakes Companies Make Using Social Media HELPING BUSINESS USE SOCIAL MEDIA MARKETING FOR A

Scholarly information – past, present and future Siân Harris Editor, Research Information

Quality Enhancement – Faculty of Human Sciences Quality Enhancement Future directions Operations + challenges Size and scope Responding Roles and communication.

Sunita Sarawagi.  Enables richer forms of queries  Facilitates source integration and queries spanning sources “Information Extraction refers to the.

2008 © ChengXiang Zhai 1 Contextual Text Analysis with Probabilistic Topic Models ChengXiang Zhai Department of Computer Science Graduate School of Library.

Blogging in America How blogs are shaping businesses and mass media in the US.

Topic Modeling with Network Regularization Qiaozhu Mei, Deng Cai, Duo Zhang, ChengXiang Zhai University of Illinois at Urbana-Champaign.

Big Data Research Progress Chao Jan 22, Big Data Lab Big MIT – – 23 nodes.

Result presentation. Search Interface Input and output functionality – helping the user to formulate complex queries – presenting the results in an intelligent.

1 Web Search and Advanced Internet Services 290N Class Introduction Tao Yang, 2014.

Temporal Event Map Construction For Event Search Qing Li Department of Computer Science City University of Hong Kong.

Welcome Introduction and Overview Computer Science Research Practicum Fall 2012 Andrew Rosenberg.

CS598CXZ Course Summary ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign.

MINING MULTI-FACETED OVERVIEWS OF ARBITRARY TOPICS IN A TEXT COLLECTION Xu Ling, Qiaozhu Mei, ChengXiang Zhai, Bruce Schatz Presented by: Qiaozhu Mei,

Automatic Construction of Topic Maps for Navigation in Information Space ChengXiang (“Cheng”) Zhai Department of Computer Science University of Illinois.

COMP 4332 / RMBI 4330 Big Data Mining (Spring 2015) Lei Chen Hong Kong University of Science and Technology

Page 1 WEB MINING by NINI P SURESH PROJECT CO-ORDINATOR Kavitha Murugeshan.

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Prepare Yourself for IR Research ChengXiang Zhai Department of Computer.

1 Information Retrieval and Advanced Internet Services 290N Class Introduction Tao Yang, 2015

Aardvark Anatomy of a Large-Scale Social Search Engine.

Social Content ASIDIC, Tampa Fl, March 2009 What is Social Content? How can we use Social Content? What is the future of Social Content?

How to make searchers better searchers Vivian Lin Dufour 21 Oct 2010.

2009 © Qiaozhu Mei University of Illinois at Urbana-Champaign Towards Contextual Text Mining Qiaozhu Mei University of Illinois at Urbana-Champaign.

2009 © Qiaozhu Mei University of Illinois at Urbana-Champaign Contextual Text Mining Qiaozhu Mei University of Illinois at Urbana-Champaign.

Introduction to Web Mining Spring What is data mining? Data mining is extraction of useful patterns from data sources, e.g., databases, texts, web,

Integrating Technology for Instruction and Learning Jennifer Verschoor & Evelyn Izquierdo April 3, 2009.

2008 © ChengXiang Zhai Dragon Star Lecture at Beijing University, June 21-30, Frame an IR Research Problem and Form Hypotheses ChengXiang Zhai Department.

 Text Representation & Text Classification for Intelligent Information Retrieval Ning Yu School of Library and Information Science Indiana University.

Dixon Jones Receptional Internet Marketing. WWW: Machine or Alive?

Intent Subtopic Mining for Web Search Diversification Aymeric Damien, Min Zhang, Yiqun Liu, Shaoping Ma State Key Laboratory of Intelligent Technology.

Comparative Text Mining Q. Mei, C. Liu, H. Su, A. Velivelli, B. Yu, C. Zhai DAIS The Database and Information Systems Laboratory. at The University of.

2010 © University of Michigan 1 DivRank: Interplay of Prestige and Diversity in Information Networks Qiaozhu Mei 1,2, Jian Guo 3, Dragomir Radev 1,2 1.

Hypersearching the Web, Chakrabarti, Soumen Presented By Ray Yamada.

Research Topics/Areas. Adapting search to Users Advertising and ad targeting Aggregation of Results Community and Context Aware Search Community-based.

Anant Pradhan PET: A Statistical Model for Popular Events Tracking in Social Communities Cindy Xide Lin, Bo Zhao, Qiaozhu Mei, Jiawei Han (UIUC)

A Day of technology Improving upon your technology skills Giving every child the opportunity to learn in a robust digital environment everyday. making.

Mining real world data Web data. World Wide Web Hypertext documents –Text –Links Web –billions of documents –authored by millions of diverse people –edited.

ReproZip Packing Experiments for Sharing and Publication Fernando Chirigati, Juliana Freire | NYU-Poly Dennis Shasha | NYU.

ESSENTIAL SCIENCE INDICATORS (ESI) James Cook University Celebrating Research 9 OCTOBER 2009 Steven Werkheiser Manager, Customer Education & Training ANZ.

Next Generation Search Engines Bin Tan. Current search engines only provide search service. Current search engines only provide search service. A lot.

© 2009 Endeca Technologies, Inc. All rights reserved. exploring semantic means Daniel Tunkelang Chief Scientist, Endeca.

Using your school Databases Please open up a word document. Please access the Library Media Center Please open up a word document. Please access the Library.

Google Custom Search Engine Presented by David Bickford Director, Arizona Health Sciences Library at the Phoenix Biomedical Campus October 21, 2015.

Supporting Knowledge Discovery: Next Generation of Search Engines Qiaozhu Mei 04/21/2005.

User Modeling and Recommender Systems: Introduction to recommender systems Adolfo Ruiz Calleja 06/09/2014.

Toward Semantic Search: RDFa based facet browser Jin Guang Zheng Tetherless World Constellation.

Social Information Processing March 26-28, 2008 AAAI Spring Symposium Stanford University

Text Information Management ChengXiang Zhai, Tao Tao, Xuehua Shen, Hui Fang, Azadeh Shakery, Jing Jiang.

Info Start-up company founded by academicians and graduate students from Sabanci University. We offer social media analysis tools and services including.

2014 Lexicon-Based Sentiment Analysis Using the Most-Mentioned Word Tree Oct 10 th, 2014 Bo-Hyun Kim, Sr. Software Engineer With Lina Chen, Sr. Software.

Social Media & Social Networking 101 Canadian Society of Safety Engineering (CSSE)

An Overview of Literature Management Systems Qiaozhu Mei April 12, 2007.

Leeds 20/06/07 TravelMole.com 1 Web 2.0 – informed opinion or a load of blog? Graham McKenzie TravelMole.com.

Library Training on Mendeley Reference Manager

CS510 Advanced Topics in Information Retrieval (Fall 2017)

MINING DEEP KNOWLEDGE FROM SCIENTIFIC NETWORKS

中国计算机学会学科前沿讲习班:信息检索 Course Overview

Elsevier Activity Range

Text Retrieval and Data Mining in SI - An Introduction

Introduction to TIMAN: Text Information Managemetn & Analysis

CS510 (Fall 2018) Advanced Topics in Information Retrieval

Fake News Detection - Social Article Fusion

Course Summary ChengXiang “Cheng” Zhai Department of Computer Science

Web Mining Department of Computer Science and Engg.

Web Search and Advanced Internet Services

Presentation transcript:

2010 © University of Michigan 1 Text Retrieval and Data Mining in SI - An Introduction Qiaozhu Mei School of Information Computer Science and Engineering University of Michigan

2010 © University of Michigan Challenge of Data Mining 2 Published Content: 3-4G/day User generated data: 8-10G/day Private text data: 3T/day - Ramakrishnan and Tomkins 2007

2010 © University of Michigan What do We Do in this Battle? 3 Crowd Context Content Social networks Online communities Academic networks Information networks time location authorship sentiments impact event Topics User Query logs Social bookmarks Scientific Literature News articles blogs tweets Web pages Social media EHR Contextual Text Mining Social Data Mining Information Retrieval Social Network Mining Health Informatics Bioinformatics Statistical Topic Modeling Web Search

2010 © University of Michigan Personalization v.s. Diversification 4 MSR PageRank Mountain Safety Research + MSR Tents + MSR Wheels + Microsoft Research …  ? Personalized Rank Microsoft Research + Microsoft Research Redmond + Microsoft Research Asia … ? Diverse Rank Mountain Safety Research + Microsoft Research + Metropolis Street Racer … ? - Joint work with Jian Guo, Qian Zhen

2010 © University of Michigan 5 Hot Topics in SIGMOD Topic Evolution and Trends What’s hot in literature/twitter?

2010 © University of Michigan 6 One Week Later Modeling Spatiotemporal Topic Diffusion How does discussion spread? Topic = “government response in hurricane Katrina”

2010 © University of Michigan 7 Tom Hanks, who is my favorite movie star act the leading role. protesting... will lose your faith by watching the movie. a good book to past time.... so sick of people making such a big deal about a fiction book The Da Vinci Code Summarizing and Tracking Opinions What is good and what is bad? Blogs; customer reviews

2010 © University of Michigan 8 Information retrieval community Machine learning community Data mining community Social/Academic Network Topical Community Detection Who works together on what? Text Content

2010 © University of Michigan Thanks! 9 -Joint work with Cheng Zhai, Ken Church, Bruce Schatz, Ravi Kumar, Andrew Tomkins, Denny Zhou, Jian Guo, Qian Zhen, Xu Ling, Duo Zhang, Deng Cai, Dong Xin, Chao Liu...