|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.|
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 2:00-3:15 pm, Manning 304|
|Instructor:||Jaime Arguello (email, web)|
|Office Hours:||T, Th 9:30-10:30am, Manning 305|
|Required Textbook:||Data Mining: Practical Machine Learning Tools and Techniques (Third Edition) Ian H. Witten, Eibe Frank, and Mark A. Hall. 2011. Morgan Kaufman. ISBN 978-0-12-374856-0. Available online or in the campus bookstore.|
|Course Policies:||Attendance, Participation, Collaboration, Plagiarism & Cheating, Late Policy|
|Grading:||10% Class participation
15% Midterm Exam
40% Homework (10% each)
35% Final project (5% project proposal, 20% project report, 10% project presentation)
|Grade Assignments:||Letter grades will be assigned using the following scale: H 95-100%, P 80-94%, L 60-79%, and F 0-59%.|
Subject to change! Readings from the required textbook (Witten, Frank, and
Hall) is marked with a WFH bellow.