Syllabus: LIN353C Introduction to computational linguistics

Course Information

Instructor Contact Information

Katrin Erk
office hours: Tuesday/Thursday 2-3:30pm
office: CLA 4.734
email: katrin dot erk at utexas dot edu

Teaching assistant

Venkata Govindarajan
office hours: Monday/Wednesday 1-2pm
email: venkatasg at utexas dot 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.

The 3rd edition is in the works. We will be using individual chapters that are available online from the book webpage.

Additional required readings will be made available for download from the course website.

Content overview

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: 

  • Word form analysis and analysis of sentence structure:
    • morphology (taking words apart)
    • part of speech (labeling words as verbs, nouns, adjectives, etc.)
    • sentence structure analysis
  • Word meaning analysis

And we focus on some of the key algorithms and data structures that are being used in computational linguistics:

  • describing regularities in letter sequences and word sequences: finite state automata and regular expressions
  • describing more complex structure: context-free grammars and parsing
  • algorithms that learn from data: machine learning
  • computing with a machine-readable dictionary
  • learning about the meaning of a word from the contexts in which it appears

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).

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

Quantitative Reasoning

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.

Course requirements and grading policy

  • 8 assignments: 80% (10% each)
  • Course project: 20%:
    • Intermediate report: 5%
    • Final report: 15%

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 approval of the instructor. Options for course projects, and more details on the project requirements are listed on the course project page.

The course will use plus-minus grading, using the following scale:

 Grade Percentage
 A >= 93%
 A- >= 90%
 B+ >= 87%
 B >= 83%
 B- >= 80%
 C+ >= 77%
 C >= 73%
 C- >= 70%
 D+ >= 67%
 D >= 63%
 D- >= 60%

Attendance is not required, and it is not used as part of determining the grade. For a good learning outcome, I would still very much encourage you to attend.

Extension Policy

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. The last day in the semester on which the class meets (Dec 5, 2019) is the last day to turn in late assignments for grading. 

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, 5121-471-6259.

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

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