Course: LIN386M Introduction to Computational Linguistics Semester: Fall 2011
Instructor Contact Information
Jason Baldridge
office hours: Mon 10-noon, Fri 9:30-10:30
office: Calhoun 510
phone: 232-7682
email: jasonbaldridge@gmail.com
Prerequisites
Syllabus and Text
This page serves as the syllabus for this course.
Additional required readings will be made available for download from the schedule page of the course website. For learning Scala, there is no official course book. I will be creating many tutorials and providing explicit instruction. In addition, here are some resources: Exams and Assignments
There will be no midterm or final exam. Instead, this course has a course project.
There will be five homework assignments. 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.
Philosophy and Goal
The foremost goal of this course is to expose the student to the core
techniques and applications of computational linguistics, with a primary
focus on symbolic approaches. Students will gain an appreciation for
the difficulties inherent in NLP and and understanding of strategies for
tackling them. The course will address both theoretical and applied
topics.
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 Scala programming language)
build and use finite state transducers with XFST
appreciate the relationship between
linguistic theory and computational applications, especially with
respect to morphology, syntax and semantics
write computational grammars
complete a non-trivial NLP project and write a report in the format of submissions to computational linguistics conferences
This course presents an opportunity for students to gain experience with
models and algorithms used in computational linguistics that underly
practical applications while gaining an appreciation for the theoretical
questions which they raise and which they can help us tackle. It will
thus help prepare the student both for jobs in the industry and for
doing original research in computational linguistics.
The course is designed to include key activities engaged in by
computational linguistics researchers, including generation of ideas and
programs, critical oral discussion of ideas, and written evaluation and
presentation of ideas. This will help students make the transition to
doing real research in the field. For those students with interest, it
could possibly lead to subsequent research opportunities.
Content Overview
This course will focus on many of the core technologies and techniques
used in computational linguistics, such as finite-state methods,
categorial grammars and parsing. It will also serve as an introduction
to Scala programming and programming for NLP.
This course provides a broad introduction to computational linguistics
with a particular emphasis on core algorithms and data structures.
Topics include:
finite-state automata and transducers
computational morphology
- n-gram language models
part-of-speech tagging
categorial grammars and parsing
feature structures and unification
computational semantics
The field of computational linguistics has experienced significant
growth in the last two decades. Some of the most important factors behind
this include the use of statistical techniques, the availability of
large (sometimes annotated) corpora (including the web itself), and the
availability of relatively cheap and powerful computers. Together, these
factors have played a major part in making computational linguistics
very relevant in applied settings. We will show, on a few chosen topics,
how statistical natural language processing builds on and uses the
fundamental data structures and algorithms presented in this course. In
particular, we will discuss:
See the course schedule for details.
Course Requirements
Course project (50%)
Course Project (50%) - Project Ideas (1%, 1 page).
- Proposal
(4%, 3 pages).
- Progress Report (10%, 6 pages).
- Final Report (25%, 8 pages).
- Final Presentation (10%).
Assignments (50%): There will be five assignments, each worth 10% each of the total course
grade. Assignments will be graded on a five-point scale, described
below.
- no credit (e.g., you failed to turn in the
assignment).
- serious deficiencies (i.e., you missed significant
portions of the assignment or a significant number of the answers
were incorrect.
- adequate completion (i.e., most of the answers were
correct, but there were some missing or incorrect answers).
- satisfactory completion (i.e., everything or nearly
everything was correct).
- extraordinary mastery (i.e., you went above and beyond
what was necessary).
Overall course grades. The grading scale is different from the usual one used in the USA. 80+ A
77-80 A-
74-77 B+
70-74 B
67-70 B-
64-67 C+
60-64 C
57-60 C-
54-57 D+
50-54 D
47-50 D-
0-47 F
This scale is inspired by typical British
grading scale. It allows us to give you a better sense of where you can
improve, taking off points, but still giving an A for quality work.
Also, if you get 90+, it means you did an amazingly good job, above and
beyond expectations. Attendance is not required, and it is not used as part of determining
the grade.
Extension Policy
Homework must be turned in on the due date in order to receive credit. Late homework will be accepted only under exceptional circumstances (e.g., medical or family emergency) and at the discretion of the instructor (e.g.
exceptional denotes a rare event). This policy allowing for
exceptional circumstances is not a right, but a privilege and courtesy
to be used when needed and not abused. Should you encounter such
circumstances, simply email assignment to instructor and note "late
submission due to exceptional circumstances". You do not need to provide
any further justification or personally revealing information regarding
the details.
Academic Honor Code
You are encouraged to discuss assignments with classmates, but all
written submission must reflect your own, original work. If in doubt,
ask the instructor. Acts like plagiarism represent a serious violation
of UT's Honor Code and standards of conduct: Students who violate University rules on academic dishonesty are subject
to severe disciplinary penalties, such as automatically failing
the course and potentially being dismissed from the University. Don't
risk it. Honor code violations ultimately harm yourself as well as other
students, and the integrity of the University,
policies on academic honesty will be strictly enforced.
For further
information please visit the Student Judicial Services Web site: http://deanofstudents.utexas.edu/sjs.
Notice about students with disabilities
The University of Texas at Austin provides appropriate accommodations
for qualified students with disabilities. To determine if you qualify,
please contact the Dean of Students at 512-471-6529 or UT Services for
Students with Disabilities. If they certify your needs, we will work
with you to make appropriate arrangements.
UT SSD Website: http://www.utexas.edu/diversity/ddce/ssd
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.
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