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: There are no prerequisites for this course. We will be using a tool called LightSIDE to train and test machine learned models for different predictive tasks. LightSIDE has a graphical user interface that makes it easy to do this without knowing how to program. That being said, knowing how to program (and manipulate text) may enable you to conduct more interesting experiments as part of your final project.
This course will involve understanding mathematical concepts and procedures. I will cover the basics in order for you to understand these. However, if you strongly dislike math and are unwilling to grapple with and ultimately conquer mathematical concepts and procedures, this may not be a good course for you.
Time & Location: M,W 1:25-2:40pm, Manning 307 (In Person).
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, Use of Generative AI Tools
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
1Wed. 1/10Class Cancelled  
2Mon. 1/15MLK Day (No Class)  
3Wed. 1/17 Introduction to Text Mining: The Big Picture 
4Mon. 1/22 Course Overview: Roadmap and ExpectationsWFH Ch. 1, Mitchell '06
5Wed. 1/24 Predictive Analysis: Concepts, Features, and Instances IWFH Ch. 2, Dominigos '12
6Mon. 1/29HW1 OutPredictive Analysis: Concepts, Features, and Instances II 
7Wed. 1/31 Text Representation I 
8Mon. 2/5 Text Representation II 
9Wed. 2/7 Machine Learning Algorithms: Naive BayesWFH Ch. 4.2, Mitchell Sections 1 and 2
10Mon. 2/12Well-Being Day (No Class)  
11Wed. 2/14HW1 Due, HW2 Out, train.csv, test.csvLighSIDE TutorialLightSIDE User Manual
12Mon. 2/19 Machine Learning Algorithms: Instance-based Classification IWFH Ch. 4.7
13Wed. 2/21Project Proposal DueMachine Learning Algorithms: Instance-based Classification II 
14Mon. 2/26 Machine Learning Algorithms: Linear Classifiers IWFH 3.2 and 4.6
15Wed. 2/28HW2 DueMachine Learning Algorithms: Linear Classifiers II 
16Mon. 3/4 Final Project Breakout Group Discussion 
21Wed. 3/6 Predictive Analysis: Experimentation and Evaluation IWFH Ch. 5
22Mon. 3/11Spring Break (No Class)  
23Wed. 3/13Spring Break (No Class)  
24Mon. 3/18 Midterm Review 
25Wed. 3/20 Midterm 
26Mon. 3/25 Predictive Analysis: Experimentation and Evaluation IISmucker et al., '07, Cross-Validation, Parameter Tunning and Overfitting
27Wed. 3/27 Predictive Analysis: Experimentation and Evaluation III  
28Mon. 4/1HW3 OutExploratory Analysis: Clustering IManning Ch. 16
29Wed. 4/3 Exploratory Analysis: Clustering II 
30Mon. 4/8 Sentiment AnalysisPang and Lee, '08 (skip Section 5 and only skim Section 6), Pang and Lee, '02
31Wed. 4/10 Discourse AnalysisArguello '15
32Mon. 4/15HW3 DueDetecting ViewpointWeibe '10
33Wed. 4/17 Text-based ForecastingLerman et al., '08
34Mon. 4/22 Final Project Presentations I 
35Wed. 4/24 Final Project Presentations II 
36Mon. 4/29 Final Project Presentations III 
37Fri. 5/3Project Due