Ling 472/CSE 472: Introduction to Computational Linguistics
Spring 2020

Course Info

Instructor Info

  Emily M. Bender Olga Zamaraeva
Office Hours: Thursdays 10-11, Fridays 10:30-11:30 TBA
Office Location: Zoom (link in Canvas) Zoom (link in Canvas)
Email: ebender at uw olzama at uw

Syllabus

Description

Learning outcomes. By the end of this course, students will:

Computational linguistics is a broad field incorporating research and techniques for processing language with computers at all levels of linguistic structure. In this class, we will survey various topics and tasks in computational linguistics focusing on linguistic structure. While we will cover some of the basics of Natural Language Processing (which we will consider a separate subfield), this class will not focus on one specific approach (such as deep learning). Students in this class are expected to have a background in either computer science or linguistics, but not necessarily both. Expect this class to be difficult at times and easy at others. We hope to offer something new and interesting for everyone.

Accesibility Policies

If you have already established accommodations with Disability Resources for Students (DRS), please communicate your approved accommodations to me at your earliest convenience so we can discuss your needs in this course.

If you have not yet established services through DRS, but have a temporary health condition or permanent disability that requires accommodations (conditions include but not limited to; mental health, attention-related, learning, vision, hearing, physical or health impacts), you are welcome to contact DRS at 206-543-8924 or uwdrs@uw.edu or disability.uw.edu. DRS offers resources and coordinates reasonable accommodations for students with disabilities and/or temporary health conditions. Reasonable accommodations are established through an interactive process between you, your instructor(s) and DRS. It is the policy and practice of the University of Washington to create inclusive and accessible learning environments consistent with federal and state law.

Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW's policy, including more information about how to request an accommodation, is available at Faculty Syllabus Guidelines and Resources. Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form available at https://registrar.washington.edu/students/religious-accommodations-request/.

[Note from Emily: The above language is all language suggested by UW and in the immediately preceding paragraph in fact required by UW. I absolutely support the content of both and am struggling with how to contextualize them so they sound less cold. My goal is for this class to be accessible. I'm glad the university has policies that help facilitate that. If there is something you need that doesn't fall under these policies, I hope you will feel comfortable bringing that up with me as well.]

COVID 19 (novel coronavirus) policy: This class will be on-line only with lectures broadcast live and also recorded in case students can't attend in real time. I'll be posting a survey in the first week of class to get your feedback on how to best set up online learning to work for your current situation. All homework can be turned in remotely. For group work, encourage students to use video conferencing as needed. There will be no in-class exams.

Requirements

Students are expected to complete the assigned readings before each lecture. Lecture and Lab/Section will connect with the readings, but not everything in the readings will be covered in lecture. Homework assignments and exams may nonetheless cover material in the readings not gone over in class.

All homework assignments and the final project will include a significant writing component, weight at or near 1/2 of the assignment grade. Be sure to save time to do a careful job on your write up.

We expect all write ups to be turned in as pdf files, even if they started as plain text files that we gave you.

Collaboration policy: Students are encouraged to work with each other on the homework, both in small groups and by posting & answering questions on Canvas. However, each student must turn in their own answers (both code and write up). No copying or sharing code or prose is allowed. Also, students who have collaborated must acknowledge the collaboration in their write ups (e.g. "I discussed this problem with Kim Smith/with classmates on Canvas as we were working on it.").

Plagiarism policy: Plagiarism is strictly forbidden. The offender will get 0 points for the plagiarized assignment and will be reported to the University. NB: It is very easy to detect not only plagiarized text but also a (piece of a) program, or even a mathematical solution that was adapted from something posted on the internet. Just don't. Submit your own solution, and rest assured, it will be unique!

Late homework policy: Unless prior arrangements are made, homework turned in late but within 24 hours of the deadline will be graded at 80% credit, homework turned between 24 and 48 hours will be graded at 70% credit, and homework turned in later than that will not be graded. No late final projects or reading questions will be accepted.

Grades will be based on (this may be updated through the start of class):

Schedule of Topics and Assignments (still under construction)

DateTopicReadingBlog optionsDue
3/31Introduction, organizationOnline learning survey
4/2Regular expressionsJM Ch 2 (through 2.4)
4/3section slides,
jupyter notebook (optional)
(opional) jupyter instructionsAssignment 0
4/7Dialogue Systems and ChatbotsJM Ch 26.1-26.3, 26.626.4, 26.5, BL #5-7, BL Ch 9
4/9Finite state methods in phonology and morphologyBender 2013, Ch 3; FOMA tutorialKarttunen 1998
4/10SlidesAssignment 1
4/14Machine Learning, Bird's Eye ViewMitchel 2017Pilehvar and Camacho-Collados (in prep), Ch 2.2
4/16Evaluation and Error AnalysisResnik & Lin 2010, Kummerfeld et al 2012Fokkens et al 2013, Wu et al 2019
4/17SlidesAssignment 2
4/21Societal Impact of NLPNathan et al 2007Gonen and Goldberg 2019, Sap et al 2019
4/23Data and Model DocumentationBender and Friedman 2018Gebru et al 2018, Mitchell et al 2019
4/24slidesProject Milestone 1
4/28N-gram Language ModelingJM Ch 3 (through 3.6)JM 3.7
4/30Neural LMsJM Ch 7 (primarily 7.5)Devlin et al 2019
5/1slides (had to reannotate)Assignment 3
5/5PCFGsJM Ch 13.1-13.2, JM Ch 14.1-14.3JM 14.6, JM 14.7
5/7Dependency ParsingJM Ch 15.1-15.3JM 15.4, JM 15.5, Nivre et al 2016
5/8SlidesAssignment 4
5/12Logical Representation of Sentence MeaningJM Ch 16.1-16.3JM 16.4, JM 16.5, BL Ch 7, BL Ch 8
5/14Grammar-Based Treebanking,Flickinger et al 2017 [audio]Bender et al 2015, Buys and Blunsom 2017, Chen et al 2018
5/15slides, code, dataProject Milestone 2
5/19Vector Semantics (RQs)JM Ch 6 (through 6.6)JM 6.7, BL #25-26
5/21Word Embeddings (additional slides)JM Ch 6.8-6.13Schluter 2018, Bloem et al 2019Pilehvar and Camacho-Collados (in prep), Ch 3
5/22slides, codeAssignment 5
5/26Linguistic Semantics and NLPERG Semantic Documentation (excluding inventory page and its subpages), Bender and Koller 2020Dua et al 2019, Niven and Kao 2019
5/28Catch-up/review/wrap-up
5/29Slides are on Canvas (under Files)Assignment 6
Project Milestone 3
6/2Term Project Presentations
6/4Term Project Presentations
6/5Term Project Presentations
THURSDAY 6/11Final projects due


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