The University of Texas at Austin
Fall 2018
Instructor: Jessy Li
This undergraduate class looks at everyday tasks that involve natural language processing: document classification, spelling and grammar correction, dialogue systems, machine translation, cryptography and forensic linguistics. Students will get insight into how these systems work (and why it is still so difficult to do natural language processing well). We also consider social and ethical considerations such as privacy, job creation and loss due to language technologies, and the nature of machine intelligence.
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 to one week in advance of their due dates.
Given that homeworks and the exams address the material covered in class, good attendance is essential for doing well in this class.
In the past decades, the widening use of computers has had a profound influence on the way ordinary people communicate, search and store information. For the overwhelming majority of people and situations, the natural vehicle for such information is natural language. Text and to a lesser extent speech are crucial encoding formats for the information revolution.
In this course, you will be given insight into the fundamentals of how computers are used to represent, process and organize textual and spoken information, as well as tips on how to effectively integrate this knowledge into working practice. We will cover the theory and practice of human language technology. Topics include text encoding, search technology, tools for writing support, machine translation, dialog systems, computer aided language learning and the social context of language technology.
This course uses natural language systems to motivate students to exercise and develop a range of basic skills in formal and computational analysis. The course philosophy is to ground abstract concepts in real world examples. We introduce strings, regular expressions, finite-state and context-free grammars, as well as algorithms defined over these structures and techniques for probing and evaluating systems that rely on these algorithms. The course goes beyond merely subjective evaluation of systems, emphasizing analysis and reasoning to draw and argue for valid conclusions about the design, capabilities and behavior of natural language systems.
Evaluation will be based on the exams, homeworks, and the essay.
Topics include:
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.
There will be seven assessed assignments, one essay, and two exams.
| 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.
Extensions will be considered on a case-by-case basis, but in most cases they will not be granted. 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. The maximum extension penalty is 40 points if handed in before the last day of class. Resubmissions of assignments are allowed; extension penalty applies for post-deadline resubmissions.
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, 512-471-6259.
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.
Notes:
Readings are given in short format, e.g. DBM pp. 1-12 means “DBM pages 1-12”, to be read by class on the date it appears.