1. Course Summary ChengXiang “Cheng” Zhai Department of Computer Science
University of Illinois at Urbana-Champaign

2. Course Goal
Advanced (graduate-level) introduction to the field of information retrieval (IR), broadly including Text mining
Goal
Provide a systematic introduction to statistical language models and their applications in text retrieval and text analysis
Provide an opportunity for students to explore frontier topics via course projects (customized toward the interests of students)
Give students enough training for doing research in IR or applying advanced IR techniques to applications
Tangible outcome: research paper, open source code, and application system

3. Grading
Assignments: 30%
Midterm 1: 20%
Midterm 2: 20%
Project: 30%

4. Text data cover all kinds of topics
People
Events
Products
Services, … …
Sources:
Blogs
Microblogs
Forums
Reviews ,…
45M reviews
53M blogs
1307M posts
65M msgs/day
115M users 10M groups …

5. Humans as Subjective & Intelligent “Sensors”
Sense
Report
Real World
Sensor
Data
Weather
Thermometer
3C , 15F, …
Perceive
Express
“Human Sensor”
Locations
Geo Sensor
41°N and 120°W ….
Network Sensor
Networks

6. Unique Value of Text Data
Useful to all big data applications
Especially useful for mining knowledge about people’s behavior, attitude, and opinions
Directly express knowledge about our world: Small text data are also useful!
Data  Information  Knowledge
Text Data

7. Text Mining & Analytics Course
Main Techniques for Harnessing Big Text Data: Text Retrieval + Text Mining
This Course
Text Retrieval
Text Mining & Analytics Course
Text Mining
Big
Text Data
Big Text Data
Small
Relevant Data
Small Relevant Data
Knowledge
Many Applications

8. Main Techniques for Building a TextScope: Text Retrieval + Text Analysis
Search engines
Filtering
Recommender
Summarization
Clustering
Categorization
Topic mining
Sentiment
… …
Prediction
Medical/Health
Education
Security
Business
Social Media
Text Retrieval
Text Analysis
Big
Text Data
Big Text Data
Small
Relevant Data
Small Relevant Data
Knowledge
Many Applications

9. This Course: Statistical Language Models
Search engines
Filtering
Recommender
Summarization
Clustering
Categorization
Topic mining
Sentiment
… …
Prediction
Medical/Health
Education
Security
Business
Social Media
Text Retrieval
Text Analysis
Big
Text Data
Big Text Data
Small
Relevant Data
Small Relevant Data
Knowledge
Many Applications

10. What to Learn/Do Next? Applications Foundation User Models
Web, Bioinformatics…
User Models
Machine Learning
Pattern Recognition
Data Mining
Human-Computer
Interaction
Library & Info
Science
Statistics
Optimization
Foundation
Computer
Vision
Information
Retrieval
Data/Info Integration
Databases
Natural
Language
Processing
System Development
Software engineering
Computer systems
Algorithms
Systems

11. Thank You All of you! Piazza contributors
TAs: Assma Boughoula, Xueqing Liu
Piazza contributors
&
All of you!