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 12:00-1:15pm, Remote
Instructor: Jaime Arguello (email, web)
Office Hours: By Appointment
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 WFHPP bellow.
Lecture Date Events Topic Reading Due
1 Mon. 8/10   Introduction to Text Mining: The Big Picture  
2 Wed. 8/12   Course Overview: Roadmap and Expectations WFHP Ch. 1, Mitchell '06, Hearst '99
3 Mon. 8/17 HW1 Out Predictive Analysis: Concepts, Features, and Instances I WFHP Ch. 2, Dominigos '12
4 Wed. 8/19   Predictive Analysis: Concepts, Features, and Instances II  
5 Mon. 8/24   Text Representation I  
6 Wed. 8/26   Text Representation II  
7 Mon. 8/31 HW1 Due Text Representation II  
8 Wed. 9/2   Machine Learning Algorithms: Naïve Bayes WFH Ch. 4.2, Mitchell Sections 1 and 2
9 Mon. 9/7 Labor Day (No Class)    
10 Wed. 9/9 HW2 Out LighSIDE Tutorial (User Manual)  
11 Mon. 9/14   Machine Learning Algorithms: Instance-based Classification WFH Ch. 4.7
12 Wed. 9/16 Final Project Proposal Due Machine Learning Algorithms: Linear Classifiers I WFH 3.2 and 4.6, Mitchell Sections 3 and 4
13 Mon. 9/21   Machine Learning Algorithms: Linear Classifiers II  
14 Wed. 9/23 HW2 Due Predictive Analysis: Experimentation and Evaluation I WFH Ch. 5
15 Mon. 9/28   Predictive Analysis: Experimentation and Evaluation II Smucker et al., '07, Cross-Validation, Parameter Tunning and Overfitting
16 Wed. 9/30   Project Report Status Meetings  
21 Mon. 10/5   Midterm Review  
22 Wed. 10/7   Midterm  
23 Mon. 10/12   Exploratory Analysis: Clustering I Manning Ch. 16
24 Wed. 10/14 HW3 Out Exploratory Analysis: Clustering II  
25 Mon. 10/19   Weka Tutorial WFH Appendix B
26 Wed. 10/21   Sentiment Analysis Pang and Lee, '08 (skip Section 5 and only skim Section 6), Pang and Lee, '02
27 Mon. 10/26   Discourse Analysis Arguello '15
28 Wed. 10/28 HW3 Due Detecting Viewpoint and Persepective Yano et al., '10, Weibe '10
29 Mon. 11/2   Text-based Forecasting O'connor et al., '10, Lerman et al., '08
30 Wed. 11/4   Project Report Status Meetings  
31 Mon. 11/9   TBD  
32 Wed. 11/11   Student Presentations  
33 Mon. 11/16 Final Project Due 11:55pm Student Presentations