INLS 613: Text Mining

Objective: Gain experience with both the theoretical and practical aspects of text mining. Learn how to build and evaluate computer programs that generate new knowledge from natural language text.
Description: Changes in technology and publishing practices have eased the task of recording and sharing textual information electronically. This increased quantity of information has spurred the development of a new field called text mining. The overarching goal of this new field is to use computers to automatically learn new things from textual data.

The course is divided into three modules: basics, principles, and applications (see details below). The third part of the course will focus on several applications of text mining: methods for automatically organizing textual documents for sense-making and navigation (clustering and classification), methods for detecting opinion and bias, methods for detecting and resolving specific entities in text (information extraction and resolution), and methods for learning new relations between entities (relation extraction). Throughout the course, a strong emphasis will be placed on evaluation. Students will develop a deep understanding of one particular method through a course project.

Prerequisites: Students should have a reasonable background in programming in a structured or object oriented programming language, such as Java or C++. "Reasonable" means either coursework or equivalent practical experience. You should be able to design, implement, debug and test small to medium sized programs. If you would like to take this course, but do not know if you meet these pre-requisites, please send me an email.
Time & Location: M,W 2:30-3:45, Manning 208
Instructor: Jaime Arguello (email, web)
Office Hours: By Appointment, Manning 10 (Garden Level)
Teaching Assistant: Heejun Kim (email, web)
Office Hours: M 1:30-2:30
Required Textbook: Data Mining: Practical Machine Learning Tools and Techniques (Fourth Edition) Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal. 2017. Morgan Kaufman. ISBN 978-0128042915. Available online
Additional Resources: Foundations of Statistical Natural Language Processing. C. Manning and H Schutze. 1999.

Introduction to Information Retrieval. C. Manning, P. Raghavan and H. Schutze. 2008.
Course Policies: Laptops, Attendance, Participation, Collaboration, Plagiarism & Cheating, Late Policy
Grading: 10% Class participation
20% Midterm Exam
30% Homework (10% each)
40% Final project (5% project proposal, 25% project report, 10% project presentation)
Grade Assignments: Undergraduate grading scale: A+ 97-100%, A 94-96%, A- 90-93%, B+ 87-89%, B 84-86, B- 80-83%, C+ 77-79%, C 74-76%, C- 70-73%, D+ 67-69%, D 64-66%, D- 60-63%, F 0-59%

Graduate grading scale: H 95-100%, P 80-94%, L 60-79%, and F 0-59%.
Topics: Subject to change! Readings from the required textbook (Witten, Frank, Hall, and Pal) is marked with a WFHP bellow.
Lecture Date Events Topic Reading Due
1 Wed. 1/10   Introduction to Text Mining: The Big Picture  
2 Mon. 1/15 MLK Day (No class)    
3 Wed. 1/17   Course Overview: Roadmap and Expectations WFHP Ch. 1, Mitchell '06, Hearst '99
4 Mon. 1/22   Predictive Analysis: Concepts, Features, and Instances I WFHP Ch. 2, Dominigos '12
5 Wed. 1/24   Predictive Analysis: Concepts, Features, and Instances II  
6 Mon. 1/29 HW1 Out Text Representation I  
7 Wed. 1/31   Text Representation II  
8 Mon. 2/5   Machine Learning Algorithms: Linear Classifiers WFHP 3.2 and 4.6
9 Wed. 2/7   Machine Learning Algorithms: Naïve Bayes WFHP Ch. 4.2, Mitchell Sections 1 and 2
10 Mon. 2/12 HW1 Due LighSIDE Tutorial LightSIDE User's Manual
11 Wed. 2/14 HW2 Out Weka Tutorial WFHP Appendix B
12 Mon. 2/19 Project Proposal Due Machine Learning Algorithms: Instance-based Classification WFHP Ch. 4.7
13 Wed. 2/21   Predictive Analysis: Experimentation and Evaluation I WFHP Ch. 5
14 Mon. 2/26   Catch-up  
15 Wed. 2/28   Catch-up  
16 Mon. 3/5   Catch-up  
17 Wed. 3/7   Midterm Review  
18 Mon. 3/12 Sping Break (No class)    
19 Wed. 3/14 Sping Break (No class)    
20 Mon. 3/19 Midterm Midterm  
21 Wed. 3/21   Predictive Analysis: Experimentation and Evaluation II Smucker et al., '07, Cross-Validation, Parameter Tunning and Overfitting
22 Mon. 3/26   Exploratory Analysis: Clustering I Manning Ch. 16
23 Wed. 3/28 HW3 Out Exploratory Analysis: Clustering II  
24 Mon. 4/2   Sentiment Analysis Pang and Lee, '08 (skip Section 5 and only skim Section 6), Pang and Lee, '02
25 Wed. 4/4   Predicting Usefulness of Reviews Kim '17
26 Mon. 4/9   Detecting Viewpoint and Persepective Yano et al., '10, Weibe '10
27 Wed. 4/11 HW3 Due Text-based Forecasting O'connor et al., '10, Lerman et al., '08
28 Mon. 4/16   Discourse Analysis Arguello '15
29 Wed. 4/18   Student Presentations  
30 Mon. 4/23   Student Presentations  
31 Wed. 4/25   Student Presentations  
33 Tue. 5/8 (8am) Final Project Due