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 1:25-2:40pm, Manning 307 (In Person). Please review our Community Standards and Mask Use Policy.
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 Wed. 8/18   Introduction to Text Mining: The Big Picture  
2 Mon. 8/23   Course Overview: Roadmap and Expectations WFH Ch. 1, Mitchell '06
3 Wed. 8/25   Predictive Analysis: Concepts, Features, and Instances I WFH Ch. 2, Dominigos '12
4 Mon. 8/30 HW1 Out Predictive Analysis: Concepts, Features, and Instances II  
5 Wed. 9/1   Text Representation I  
6 Mon. 9/6 Labor Day (No Class)    
7 Wed. 9/8   Text Representation II  
8Mon. 9/13 Catch-up 
9Wed. 9/15 Catch-up 
10Mon. 9/20HW2 OutLighSIDE Download (needed for HW2)LightSIDE User Manual
11Wed. 9/22 Machine Learning Algorithms: Na�ve BayesWFH Ch. 4.2, Mitchell Sections 1 and 2
12Mon. 9/27Final Project Proposal DueMachine Learning Algorithms: Instance-based ClassificationWFH Ch. 4.7
13Wed. 9/29 Final Project Breakout Group Discussion I 
14Mon. 10/4HW2 DueMachine Learning Algorithms: Linear Classifiers I WFH 3.2 and 4.6
15Wed. 10/6 Machine Learning Algorithms: Linear Classifiers II 
16Mon. 10/11 Midterm Review 
21Wed. 10/13 Midterm 
22Mon. 10/18 Predictive Analysis: Experimentation and Evaluation IWFH Ch. 5
23Wed. 10/20HW3 OutPredictive Analysis: Experimentation and Evaluation II Smucker et al., '07, Cross-Validation, Parameter Tunning and Overfitting
24Mon. 10/25 Exploratory Analysis: Clustering IManning Ch. 16
25Wed. 10/27 Final Project Breakout Group Discussion II 
26Mon. 11/1 Exploratory Analysis: Clustering II 
27Wed. 11/3HW3 DueSentiment Analysis IPang and Lee, '08 (skip Section 5 and only skim Section 6), Pang and Lee, '02
28Mon. 11/8 Sentiment Analysis II 
29Wed. 11/10 Discourse AnalysisArguello '15
30Mon. 11/15 Detecting ViewpointWeibe '10
31Wed. 11/17 Text-based ForecastingLerman et al., '08
32Mon. 11/22 Final Project Presentations I 
33Wed. 11/24Thanksgiving (No Class)  
34Mon. 11/29 Final Project Presentations II 
35Wed. 12/1 Final Project Presentations III 
36Fri. 12/3Final Project Due