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.
Recommended additional text: Mark Lutz and David Ascher, Learning Python, O'Reilly.
There will be one mid-term exam and one final exam. The midterm will consist of the material covered in the first half of the class, and the final will be comprehensive, but with a greater emphasis on the contents covered in the second half of the class.
Assignments will be updated on the assignments page. There will be 6 assignments. A tentative schedule for the entire semester is posted on the schedule page. Readings and exercises may change up one week in advance of their due dates.
The homework assignments will mostly focus on programming in Python (first some simple general-purpose programs, later programs that process language data). The midterm exam will also mostly focus on Python programming. The final exam will focus on the theoretical learning goals.
Attendance is not required. However, given that we will do a lot of hands-on exercises in class, and homeworks and the exams address the material covered in class, good attendance is essential for doing well in this class.
Computational linguistics is an interdisciplinary field drawing on both linguistics and computer science. Doing computational linguistics thus involves some knowledge of both fields. 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. The focus will be on hands-on exploration, using the Python programming language and the Natural Language Toolkit. Students will gain an appreciation for the difficulties inherent in natural language processing (NLP) and and understanding of strategies for tackling them.
Some specific goals of the course are to enable students to:
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.
This course provides a broad introduction to computational linguistics with an emphasis on a practical introduction to relevant techniques. Topics include:
A detailed schedule for the course, with topics for each lecture, is available at the schedule page, which forms part of the syllabus.
Final grades will not use plus/minus grades.
If you turn in your assignment late, expect points to be deducted. Extensions will be considered on a case-by-case basis, but in most cases they will not be granted.
For other 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. For example, an assignment due at 2pm on Tuesday will have 5 points deducted if it is turned in late but before 2pm on Thursday. It will have 6 points deducted if it is turned in by 2pm Friday, etc.
The greater the advance notice of a need for an extension, the greater the likelihood of leniency.
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.
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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