Syllabus: LIN353C Introduction to computational linguistics
Course: Introduction to Computational Linguistics, LIN353C - 39150
Semester: Fall 2020
Course times: Tuesday, Thursday 12:30-2pm
Course location for in-person sessions: JGB 2.216.
Online sessions will be via Zoom, with links provided on Canvas.
Course on Canvas: https://utexas.instructure.com/courses/1289696
Instructor Contact Information
office hours: Monday 1-2, Tuesday and Thursday 2-3, via Zoom. Zoom links are on Canvas, or email me about them.
office: CLA 4.734
email: katrin dot erk at utexas dot edu
office hours: Monday and Wednesday 4-5pm, and by appointment via zoom. Zoom links for the office hours will be posteds on Slack.
email: eric dot s dot holgate at gmail dot com
Hybrid Class Main Facts
The main points to know about the hybrid class are summarized on this page.
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.
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 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 uses mathematical and computational methods to describe how language works, and it develops methods for automatic language understanding and for language technology applications. Computational linguistics is an interdisciplinary field between linguistics to computer science.
This course gives an introduction to central problems and methods in computational linguisticsin theory and practice. The course includes hands-on exercises with language processing techniques. The course also includes a short introduction to programming using the Python programming language.
Here are some of the main linguistic phenomena we look at:
semantic relations between words, such as synonymy and antonymy
word meaning similarity
parts of speech (verb, noun, adjective, ...)
the syntactic structure of sentences
Here are some core algorithms and data structures in linguistics that we will be discussing:
regularities in letter sequences and word sequences: finite state automata and regular expressions
more complex structure: context-free grammars and parsing
algorithms that learn from data: machine learning
patterns of word usage, and using them to approximate lexical meaning
patterns of typicality in word sequences
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.
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)
4 "food for thought" assignments (5% each)
Contents of the 8 main assignments:
questions that involve using NLP algorithms and data structures, both by hand and through programming
small-scale NLP applications
Content of "food for thought" assignments:
1-2 questions that can be answered through a paragraph of text. This may be questions that ask you to reflect on larger questions, or questions that ask you to try out an existing language technology application and report on your experience. Three of the four "food for thought" assignments will be in preparation for one of the in-person discussion sessions, and will be assignments designed to facilitate in-class discussion. The first "food for thought" assignment will be after the very first in-person session.
This course does not have a final exam or midterm exam.
The course will use plus-minus grading, using the following scale:
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, ideally synchronously.
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 yes. The last day in the semester on which the class meets (Dec 3, 2020) is the last day to turn in late assignments for grading.
Safety and Class Participation/Masks
We will all need to make some adjustments in order to benefit from in-person classroom interactions in a safe and healthy manner. Our best protections against spreading COVID-19 on campus are masks (defined as cloth face coverings) and staying home if you are showing symptoms. Therefore, for the benefit of everyone, this is means that all students are required to follow these important rules.
Every student must wear a cloth face-covering properly in class and in all campus buildings at all times.
Students are encouraged to participate in documented daily symptom screening. This means that each class day in which on-campus activities occur, students must upload certification from the symptom tracking app and confirm that they completed their symptom screening for that day to Canvas. Students should not upload the results of that screening, just the certificate that they completed it. If the symptom tracking app recommends that the student isolate rather than coming to class, then students must not return to class until cleared by a medical professional.
If a student is not wearing a cloth face-covering properly in the classroom (or any UT building), that student must leave the classroom (and building). If the student refuses to wear a cloth face covering, class will be dismissed for the remainder of the period, and the student will be subject to disciplinary action as set forth in the university’s Institutional Rules/General Conduct 11-404(a)(3). Students who have a condition that precludes the wearing of a cloth face covering must follow the procedures for obtaining an accommodation working with Services for Students with Disabilities.
Sharing of Course Materials is prohibited
No materials used in this class, including, but not limited to, lecture hand-outs, videos, assessments (quizzes, exams, papers, projects, homework assignments), in-class materials, review sheets, and additional problem sets, may be shared online or with anyone outside of the class unless you have my explicit, written permission. Unauthorized sharing of materials promotes cheating. It is a violation of the University’s Student Honor Code and an act of academic dishonesty. I am well aware of the sites used for sharing materials, and any materials found online that are associated with you, or any suspected unauthorized sharing of materials, will be reported to Student Conduct and Academic Integrity in the Office of the Dean of Students. These reports can result in sanctions, including failure in the course.
FERPA and Class Recordings
Class recordings are reserved only for students in this class for educational purposes and are protected under FERPA. The recordings should not be shared outside the class in any form. Violation of this restriction by a student could lead to Student Misconduct proceedings.
To help keep everyone at UT and in our community safe, it is critical that students report COVID-19 symptoms and testing, regardless of test results, to University Health Services, and faculty and staff report to the HealthPoint Occupational Health Program (OHP) as soon as possible. Please see this link to understand what needs to be reported. In addition, to help understand what to do if a fellow student in the class (or the instructor or TA) tests positive for COVID, see this University Health Services link.
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 http://www.utexas.edu/emergency.
Behavior Concerns Advice Line (BCAL)
If you are worried about someone who is acting differently, you may use the Behavior Concerns Advice Line to discuss by phone your concerns about another individual's behavior. This service is provided through a partnership among the Office of the Dean of Students, the Counseling and Mental Health Center (CMHC), the Employee Assistance Program (EAP), and The University of Texas Police Department (UTPD). Call 512-232-5050 or visit http://www.utexas.edu/safety/bcal