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 Canvas site.

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 Canvas course site and select the assignment corresponding to the project (e.g. Assignment 1 corresponds to Project 1). Click 'Submit assignment' button to open submission portal, then click 'Choose file' and select your compressed project directory 'project.tgz' created in the previous step. Finally, click the 'Submit assignment' button below.

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!

Readings

The primary recommended texts for this course are:

• Jurafsky and Martin, Speech and Language Processing, 2nd edition ONLY [amazon]
• Manning and Schuetze, Foundations of Statistical Natural Language Processing [amazon] [online]

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

Syllabus [subject to substantial change!]