LIN/CSE 467/567: Computational Linguistics
Instructor Name: Dr. Cassandra Jacobs
Class Day and Time: MWF 11AM-12PM
Location: Clemens 19
Number of Credits: 3-4 units
Email Address: email@example.com
Office Location: 614 Baldy Hall
Office Hours: Monday 12-2pm or by appointment
This course aims to provide students with an overview of the key areas which make up the field called Computational Linguistics, an understanding of the major challenges of the field as well as the major application areas for language processing techniques, and the skills to implement fundamental language processing algorithms. This course is dual listed between CSE 467/567 and LIN 467/567.
Required Text and Materials
All reading materials will be made available on Brightspace as well as the course webpage and will consist primarily of readings from the 3rd Edition of the Jurafsky and Martin (SLP3) book Speech and Language Processing: https://web.stanford.edu/~jurafsky/slpdraft/, version published 2024-01-05. The book is freely available. The text is used and referenced in lectures, as well as take-home exams.
Theme: Annotation and Error Analysis
Modern computational linguistics would not exist without high-quality annotated data and the people who create it. Many resources and tasks are the result of carefully curated datasets. But, very little attention is given in computational linguistics courses, despite data being a prerequisite for model creation and evaluation. The annotation assignments are individual annotation tasks using the python-based annotation software Doccano.
Three assignments will teach you about the challenges of different types of linguistic data, what about these problems can trip up natural language processing systems, and ways that human error and disagreement can influence our ability to draw conclusions at all. While annotations are performed individually, you will participate in a brief survey on Brightspace about your experience with that task and an assessment of your performance.
Finally, you will be part of a group that will submit a proposal for an annotation study of your own, which will culminate in a short paper and a presentation.
|Course Learning Outcome
|Mastery of linguistic constructs
|Lectures, data annotation exercises, exams
|Attending lectures and completing assignments
|Mastery of concepts in computational linguistics
|Lectures, data annotation exercises
|Mastery of natural language data annotation
|Group annotation assignments
|Submission of in-class annotation results and reflection assignments
|Mastery of computational linguistics tools
|Lectures with live coding, course notebooks
The course is held synchronously and it is expected that you will contribute to the course community. Lectures are presented as Jupyter notebooks with Python code. Documented engagement with the course material and with other students is critical for a fun and fulfilling experience in the classroom for everyone — even asking or answering “basic” questions advances our learning goals.
Assignments will be submitted through Brightspace.
|Take-home exams (3)
|Group annotation project and presentation
|Attendance, participation, and clear communication about any relevant absences
I guarantee minimum grades — students are never curved down below the numeric grade they receive in the course. Depending on the distribution of scores, undergraduates and graduate students may be graded to a slightly different curve and some questions on assignments will be required for graduate students but bonus for undergraduate students. Here are the cutoffs for the grade categories:
Lecture and reading schedule
Week 1 (January 24 & 26) - Welcome!
- W: Course content, course structure, syllabus, ethics
- F: Discussion: The promise and pitfalls of ChatGPT
Week 2 (January 29, January 31, February 2) - Prerequisites
: Computational linguistics pre-test (participation)
- Frequentist statistics
- Probability theory and Bayes’ rule
- Python idiosyncrasies
- Common NLP toolkits and comparisons
Week 3 (February 5, 7, 9) - Statistical language modeling 1
- What is a corpus? What are corpora?
- Whitespace, Unicode, punctuation - What is a “word”?
- Computing n-gram statistics
- Readings: Chapter 2
Week 5 (February 19, 21, 23) - Tokenization and morphology
- Learning and producing morphological structure
- Finite state automata and finite state transducers
- Hidden Markov Models
- Take-home exam: Tokenization - February 21 12:01 am to February 28 11:59 pm
- Readings: Appendix A
- Week 9 (March 18, 20, 22) - Spring Break! No class.
Week 10 (March 25, 27, 29) - Lexical semantics 1
- Take home exam: Syntactic parses across genres - March 27 12:01am to April 3, 11:59pm
- Propositional representations of word meaning
- Semantic features and semantic knowledge
- Annotation (Due 11:59pm April 3): Word senses
- Week 13 (April 15, 17, 19) - NLP for low-resource languages
- Effect of typological properties on computational linguistics systems
- Using “high-resource” languages to boost low-resource performance
- Multilingual neural language models
- Annotation (Due 11:59pm April 24): Error types in machine translation
- Readings: TBD
Week 14 (April 22, 24, 26) - Evaluation metrics
- Generating text
- Computing performance
- Inter-annotator agreement
- Best practices for statistical NLP
- Readings: TBD
- : Computational linguistics post-test (Participation)
- Take-home exam: Computing inter-annotator agreement - May 1 12:01am to May 8 11:59pm
- Week 15 (April 29, May 1, May 3) - Student request and Team Project presentations
- Week 16 (May 6) - Team Project presentations
Group annotation project guidelines
You will be placed in a group of approximately even teams composed of at least one LIN 567 student, one CSE 567 student, and one undergraduate (467). With appropriate written consent of the instructor, this project may be used to fulfill the Capstone requirement for the MS in Computational Linguistics. Students in a team are expected to participate equitably to the best of their ability and must communicate with the Instructor about potential team conflicts.
Accessibility Services and Student Resources:
If you have a disability and may require some type of instructional and/or examination accommodation, please inform me early in the semester so that we can coordinate the accommodations you may need. If you have not already done so, please contact the Office of Accessibility Services (formerly the Office of Disability Services) University at Buffalo, 60 Capen Hall, Buffalo, NY 14260-1632; email: firstname.lastname@example.org Phone: 716-645-2608 (voice); 716-645-2616 (TTY); Fax: 716-645-3116; and on the web at http://www.buffalo.edu/studentlife/who-we-are/departments/accessibility.html. All information and documentation is confidential.
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and will be considered a violation of UB’s academic integrity policy. Details of what resources are allowed will be provided for each assignment. If you are unsure if a resource or tool is allowable, be sure to ask.
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To effectively participate in this course, regardless of mode of instruction, the university recommends you have access to a Windows or Mac computer with webcam and broadband. Your best opportunity for success in the blended UB course delivery environment (in-person, hybrid and remote) will require these minimum capabilities.
You should spend some time following the instructions to install Doccano on your local machine:
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- Should a student need to miss class due to illness, isolation or quarantine, they are required to notify their faculty to make arrangements to make up missed work.
- Students are responsible for following any additional directives in settings such as labs, clinical environments etc.