office hours: Tuesday/Thursday 12:30-2
This page serves as the syllabus for this course.
Textbook: Jurafsky, D. and J. H. Martin, Speech and language processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition (2nd Edition). Prentice-Hall, 2008.
Additional required readings will be made available for download from the course website.
Text is everywhere, in huge amounts: Books, emails, web pages, scientific papers, and so on. To be able to use the information laid down in all this text, we need technology that can help us manage, understand, sort, and make sense of all the information, for example: Automatically translating texts from one language to another; building better search engines that can deal with complex questions instead of just keywords; figuring out automatically whether the blogs are saying good or bad things about a particular product; extracting useful facts from repositories of scientific papers about medicine.
Computational linguistics is about using mathematical and computational
methods to better describe how language works. It is about developing
algorithms for automatic language understanding. And it is about
building language technology applications. As you can see, computational
linguistics spans a wide range of questions, from linguistics to
computer science. It also draws on other fields such as cognition, and
philosophy of language.
In this course, we look at the whole spectrum of questions, from linguistic to computational. We focus on some of the main problems in automatic natural language analysis and understanding:
And we focus on some of the key algorithms and data structures that are being used in computational linguistics:
The aim of this course is to
provide a gentle and thorough introduction to the core methods and
problems in computational linguistics for students with some background
in linguistics, but no background in programming and computer science. What I mean by "gentle" is that the course includes an introduction to programming in Python, and does a lot of hands-on exploration, using the Python programming
language and the Natural Language Toolkit. What I mean by "thorough" is that I want students to understand the main ideas, the advantages and limitations of all the algorithms and data structures that I introduce in-depth (after all, the course does carry a Quantitative Reasoning flag).
This course carries the Quantitative Reasoning flag. Quantitative
Reasoning courses are designed to equip you with skills that are
necessary for understanding the types of quantitative arguments you will
regularly encounter in your adult and professional life. You should
therefore expect a substantial portion of your grade to come from your
use of quantitative skills to analyze real-world problems.
Assignments will be made available on Canvas. A tentative
schedule for the entire semester is posted on the schedule page. The homework assignments will be a mixture of programming assignments (appropriate to beginners), questions that involve using NLP algorithms and data structures by hand (as opposed to writing programs that implement them), and more theoretical learning goals.
This course does not have a final exam or midterm exam.
This course has a course project. Requirements
for the course project are: an
intermediate project report, and a final project report. Students will also get to discuss their projects in the last week of classes. By
default, course projects should be done by teams of 2 students; if you would like to work in a larger or smaller group, you need prior
of the instructor.
Options for course projects, and more details on the project requirements are listed on the course project page.
Attendance is not required. However, given that we will do a lot of hands-on exercises in class, and assignments and projects address the material covered in class, good attendance is essential for doing well in this class.
Final grades will use plus/minus grades.
If you turn in your assignment late and we have not agreed on an extension beforehand, expect points to be
deducted. Extensions will be
considered on a case-by-case basis. I urge you to let me know if you are in need of an extension, such that we can make sure that you get the time necessary to complete the assignments.
If an extension has not been agreed on beforehand, then for assignments, by default, 5 points (out of 100) will be
deducted for lateness, plus an additional 1 point for every 24-hour
period beyond 2 that the assignment is late.
Note that there are always some points to be had, even if you turn in your assignment late. So if you would like to know if you should still turn in the assignment even though it is late, the answer is always yes.
You are encouraged to discuss assignments with classmates. But all written work must be your own. Students caught cheating will automatically fail the course. If in doubt, ask the instructor.
The University of Texas at Austin provides upon request appropriate academic accommodations for qualified students with disabilities. Please contact the Division of Diversity and Community Engagement, Services for Students with Disabilities, 471-6259.
A student who misses an examination, work assignment, or other project due to the observance of a religious holy day will be given an opportunity to complete the work missed within a reasonable time after the absence, provided that he or she has properly notified the instructor. It is the policy of the University of Texas at Austin that the student must notify the instructor at least fourteen days prior to the classes scheduled on dates he or she will be absent to observe a religious holy day. For religious holy days that fall within the first two weeks of the semester, the notice should be given on the first day of the semester. The student will not be penalized for these excused absences, but the instructor may appropriately respond if the student fails to complete satisfactorily the missed assignment or examination within a reasonable time after the excused absence.
Occupants of buildings on The University of Texas at Austin campus are
required to evacuate buildings when a fire alarm is activated.
Alarm activation or announcement requires exiting and assembling
outside. Familiarize yourself with all exit doors of each classroom and
building you may occupy. Remember that the nearest exit door may not be
the one you used when entering the building. Students requiring
assistance in evacuation shall inform their instructor in writing during
the first week of class. In the event of an evacuation, follow the
instruction of faculty or class instructors. Do not re-enter a building
unless given instructions by the
following: Austin Fire Department, The University of Texas at Austin
Police Department, or Fire Prevention Services office. Information
regarding emergency evacuation routes and emergency procedures can be