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 11:15am-12:30pm, Davie 101 (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
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
1Mon. 1/9 Introduction to Text Mining: The Big Picture 
2Wed. 1/11 Course Overview: Roadmap and ExpectationsWFH Ch. 1, Mitchell '06
3Mon. 1/16MLK Day (No Class)  
4Wed. 1/18 Predictive Analysis: Concepts, Features, and Instances IWFH Ch. 2, Dominigos '12
5Mon. 1/23HW1 OutPredictive Analysis: Concepts, Features, and Instances II 
6Wed. 1/25 Text Representation I 
7Mon. 1/30 Text Representation II 
8Wed. 2/1 LighSIDE TutorialLightSIDE User Manual
9Mon. 2/6HW1 DueMachine Learning Algorithms: Na´ve Bayes IWFH Ch. 4.2, Mitchell Sections 1 and 2
10Wed. 2/8HW2 OutMachine Learning Algorithms: Na´ve Bayes II 
11Mon. 2/13Well-Being Day (No Class)  
12Wed. 2/15 Machine Learning Algorithms: Instance-based Classification IWFH Ch. 4.7
13Mon. 2/20Project Proposal DueMachine Learning Algorithms: Instance-based Classification II 
14Wed. 2/22HW2 DueFinal Project Breakout Group Discussion I 
15Mon. 2/27 Machine Learning Algorithms: Linear Classifiers I WFH 3.2 and 4.6
16Wed. 3/1 Machine Learning Algorithms: Linear Classifiers II 
21Mon. 3/6Midterm ReviewMidterm Review 
22Wed. 3/8MidtermMidterm 
23Mon. 3/13Spring Break (No Class)  
24Wed. 3/15Spring Break (No Class)  
25Mon. 3/20 Predictive Analysis: Experimentation and Evaluation IWFH Ch. 5
26Wed. 3/22 Predictive Analysis: Experimentation and Evaluation II Smucker et al., '07, Cross-Validation, Parameter Tunning and Overfitting
27Mon. 3/27HW3 OutFinal Project Breakout Group Discussion II 
28Wed. 3/29 Exploratory Analysis: Clustering IManning Ch. 16
29Mon. 4/3 Exploratory Analysis: Clustering II 
30Wed. 4/5 Sentiment AnalysisPang and Lee, '08 (skip Section 5 and only skim Section 6), Pang and Lee, '02
31Mon. 4/10HW3 DueDiscourse AnalysisArguello '15
32Wed. 4/12 Detecting ViewpointWeibe '10
33Mon. 4/17 Text-based ForecastingLerman et al., '08
34Wed. 4/19 Final Project Presentations I 
35Mon. 4/24 Final Project Presentations II 
36Wed. 4/26 Final Project Presentations III 
37TBDFinal Project Due