Syllabus for Introduction to Computational Linguistics: LIN350, Spring 2012

Course Information

  • Course: Introduction to Computational Linguistics, LIN350 - 40790
  • Semester: Spring 2012
  • Course page: http://www.katrinerk.com/courses/introduction_to_computational_linguistics_spring_2012
  • Course times: Tuesday, Thursday 2:00-3:30pm
  • Course location: MEZ 1.202

Instructor Contact Information

  • office hours: Monday 1-3pm, Tuesday 10-11am, and by appointment
  • office: Calhoun 512
  • phone: 471-9020
  • fax: 471-4340
  • email: katrin dot erk at mail dot utexas dot edu

Teaching Assistant Contact Information

  • Zach Childers
  • Office hours: Wednesday 10-11:30am, Thursday 10:30-12am, and by appointment
  • Office: Calhoun 431A
  • email: zwc at mail dot utexas dot edu

Lab information

To get an account on the computational linguistics lab machines,  send me an email to katrin dot erk at mail dot utexas dot edu.

You can then log in remotely to iliad.ling.utexas.edu or odyssey.ling.utexas.edu.


Upper-division standing.

Syllabus and Text

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.

Exams and Assignments

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. 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.

Philosophy and Goals

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:

  • understand core algorithms and data structures used in NLP
  • write non-trivial programs for NLP (using the Python programming language)
  • appreciate the relationship between linguistic theory and computational applications
  • gain insight into the possibilities and difficulties of automatic meaning analysis of text

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.

Content Overview

This course provides a broad introduction to computational linguistics with an emphasis on a practical introduction to relevant techniques. Topics include:

  • analyzing and using co-occurrences of words in text
  • finite-state automata
  • part-of-speech tagging and chunking
  • context-free grammars, and parsing
  • lexical semantics
  • an introduction to programming in the Python programming language
  • an introduction to the use of the Natural Language Toolkit
  • applications that use computational linguistics

A detailed schedule for the course, with topics for each lecture, is available at the schedule page, which forms part of the syllabus.

Course Requirements and Grading Policy

  • Assignments: 60%
  • Mid-term Exam: 20%. There will be a take-home mid-term exam on Friday March 8 over the material covered in class up to March 6.
  • Final Exam: 20%. The final exam will be given on Saturday May 12 and will cover all course material.

Final grades will not use plus/minus grades.

Extension Policy

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.

Academic Dishonesty Policy

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.

Notice about students with disabilities

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.

Notice about missed work due to religious holy days

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.

Emergency Evacuation Policy

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 found at http://www.utexas.edu/emergency.