11-711: Algorithms for NLP, Fall 2017

Instructors: Taylor Berg-Kirkpatrick and Robert Frederking
Lecture: Tuesday and Thursday 1:30pm-2:50pm, DH 1212
Recitation: Friday 1:30pm-2:20pm, MM 103
Office Hours: Taylor - Friday 12pm-1pm, GHC 6403
                      Bob - by appointment
TAs: Hieu Pham, Akshay Srivatsan and Maria Ryskina
Office Hours: Hieu - Monday 2pm-3pm, GHC 6418 (tentative)
                      Akshay - Wednesday 2pm-3pm, GHC 6418 (tentative)
                      Maria - Thursday 3:30pm-4:30pm, GHC 6603  
Forum: Piazza


9/19/17:  Project 1 now due Saturday 9/23 by 11:59pm. Submission details to follow
9/15/17:  Slides from the third recitation posted here: Implementation Tricks Slides.
9/8/17:  Notes from the second recitation posted here: KN Recitation Notes. (updated 9/10)
9/1/17:  Project 1 has been released.
9/1/17:  Slides from the first recitation posted here: Project Setup Recitation Slides.
8/31/17:  Piazza link posted above.
8/25/17:  First lecture will be on Tuesday 8/29 at 1:30pm in DH 1212.

Course Description

This course will explore current statistical techniques for the automatic analysis of natural (human) language data. The dominant modeling paradigm is corpus-driven statistical learning, with a split focus between supervised and unsupervised methods.  This term we are making Algorithms for NLP a lab-based course. Instead of homeworks and exams, you will complete four hands-on coding projects. This course assumes a good background in basic probability and a strong ability to program in Java. Prior experience with linguistics or natural languages is helpful, but not required.  There will be a lot of statistics, algorithms, and coding in this class.

Slides, materials, and projects for this new iteration of Algorithms for NLP are mainly borrowed from Dan Klein at UC Berkeley.

Project Submission

Submit projects using the class blackboard site. (link to appear soon)

1. Prepare a directory named 'project' containing no more than 3 files: (a) a jar named 'submit.jar', (b) a pdf named 'writeup.pdf', and (c) an optional jar named 'best.jar'. The jar named 'submit.jar' should contain your implementation of the core project that passes the basic requirements. For example, for project 1, the jar named 'assign1-submit.jar' is all that you would need to turn in -- renaming it 'submit.jar'. The pdf 'writeup.pdf' should contain your writeup for the project. Finally, the file 'best.jar' is an optional additional jar that implements the core project, but need not pass spot-checks. Include this last jar if you wish to demonstrate an improvement over the basic project, possibly using approximations are alternative models.

2. Compress the 'project' directory you created in the last step using the command 'tar cvfz project.tgz project'.

3. Click on the assignments tab of the main blackboard course site and select the assignment corresponding to the project (e.g. Assignment 1 corresponds to Project 1). Click the 'browse my computer' button and select your compressed project directory 'project.tgz' created in the previous step. Finally, click the submit button.

Project Grading

Projects out of 10 points total:
6 Points: Successfully implemented what we asked
2 Points: Submitted a reasonable write-up
1 Point: Write-up is written clearly
1 Point: Substantially exceeded minimum metrics
Extra Credit: Did non-trivial extension to project

Late Day Policy

Each student will be granted 5 late days to use over the duration of the semester. There are no restrictions on how the late days can be used (e.g. all 5 could be used on one project.) Using late days will not affect your grade. However, projects submitted late after all late days have been used will receive no credit. Be careful!


The primary recommended texts for this course are:

Note that M&S is free online (may need to setup proxy).  Also, make sure you get the purple 2nd edition of J+M, not the white 1st edition.

Note to Students

Take care of yourself! As a student, you may experience a range of challenges that can interfere with learning, such as strained relationships, increased anxiety, substance use, feeling down, difficulty concentrating and/or lack of motivation. All of us benefit from support during times of struggle. There are many helpful resources available on campus and an important part of having a healthy life is learning how to ask for help. Asking for support sooner rather than later is almost always helpful. CMU services are available, and treatment does work. You can learn more about confidential mental health services available on campus at: http://www.cmu.edu/counseling/. Support is always available (24/7) from Counseling and Psychological Services: 412-268-2922.

Accommodations for Students with Disabilities:

If you have a disability and have an accommodations letter from the Disability Resources office, I encourage you to discuss your accommodations and needs with me as early in the semester as possible. I will work with you to ensure that accommodations are provided as appropriate. If you suspect that you may have a disability and would benefit from accommodations but are not yet registered with the Office of Disability Resources, I encourage you to contact them at access@andrew.cmu.edu.

Syllabus [subject to substantial change!]

Week Date Topics Readings Assignments (Out)
1 Aug 29 Course Introduction J+M 1, M+S 1-3
Aug 31 Language Modeling I J+M 4, M+S 6, Chen & Goodman, Interpreting KN P1: Language Modeling (Due Sept 23)
2 Sept 5 Language Modeling II Massive Data, Bloom, Perfect, Efficient LMs  
Sept 7 Language Modeling III  
3 Sept 12 Speech Recognition I J+M 7
Sept 14 Speech Recognition II J+M 9, Decoding
4 Sept 19 Speech Recognition III, HMMs
Sept 21 POS Tagging, NER, CRFs J+M 5, Brants, Toutanova & Manning  
5 Sept 26 Parsing I M+S 3.2, 12.1, J+M 13
Sept 28 Parsing II M+S 11, J+M 14, Best-First, A*, Unlexicalized  
6 Oct 3 Parsing III Split, Lexicalized, K-Best A*, Coarse-to-fine
Oct 5 Parsing IV  
7 Oct 10 Parsing V
Oct 12 Formal Grammar  
8 Oct 17 Structured Classification I Pegasos, Cutting Plane
Oct 19 Structured Classification II  
9 Oct 24 Structured Classification III
Oct 26 Structured Classification IV J+M 16, 18, 19, Adagrad, Subgradient SVM  
10 Oct 31 Machine Translation: Alignment I J+M 25, IBM Models, HMM, Agreement, Discriminative
Nov 2 Machine Translation: Alignment II
11 Nov 7 Machine Translation: Phrase-Based Decoding
Nov 9 Machine Translation: Syntactic Hiero, String-Tree, Tree-String, Tree-Tree  
12 Nov 14 Morphology; Features and Unification J+M 3, J+M 15 (Note: errors in textbook)
Nov 16 Semantics and Discourse I J+M 17, J+M 18 (Note: errors in textbook)
13 Nov 21 Semantics and Discourse II J+M 12.7.2
Nov 23 Thanksgiving Day
14 Nov 28 Semantics and Discourse III J+M 19, 20.6-20.9
Nov 30 Semantics and Discourse IV J+M 21
15 Dec 5 Applications TBA
Dec 7 Applications TBA