Machine Learning for Naturl Languge Processing Data - Winter 2010
Project Guidelines

The projects are the most significant part of your grade in this class (50%), and the best way to explore and apply the material covered. The key to a successful project is to start early and keep a steady pace throughout the semester. Projects can be done individually or in teams of two.

Important due dates:: Proposal: February 4, 2011; Milestone: March 4, 2011; Report: April 15, 2010

Topics

The hardest part is picking a project topic. Ideally, you will pick something exciting to you, possibly related to problems you already know and care about. Typical projects fall into the following categories:

Application: Apply techniques you learned in class (or other known NLP techniques) to a novel task, or area that interests you. This often involves a large component of data collection and analysis.
Bake-off: Take several known algorithms/techniques and carefully compare them experimentally and/or theoretically on several standard (or novel) problems and datasets.
Algorithm: Develop a new method or algorithm for NLP problems. Often this results from combining ideas from several known algorithms in an interesting manner, or "lifting" techniques from other areas of computer science, statistics, physics, etc.
Analysis: Theoretically analyze and prove interesting properties of a known algorithm.

Take a look at some recent NLP research papers for inspiration. ACL Anthology , NIPS and ICML are three of the main resources where natural language processing is published, with many of the recent papers online. Of course, conferences on natural language processing and machine learning, and many others have a lot of related papers as well.

Evaluation

Project grade breakdown:: Proposal: 10%; Milestone: 30%; Report: 60%

Project proposal

Project proposals should be emailed to me by noon on February 4, 2011. Please send it in simple ASCII format, no attachments. Include the title of the project, and the name/email of other person in your group if you are a group of two and about 250-500 word description of the project, including the problems/tasks, algorithms/techniques, datasets/resources, relevant papers/references you plan to use/address. Also include what challenges you expect to arise and what you plan to accomplish by the milestone submission. Please send one email per group.

Milestone report

Project milestone reports should be emailed as PDF attachments to me by noon on March 4, 2011. The length of report should be at most 3 pages. Please include the names of the team members and the title the project. The milestone report is designed to help you keep on-track and for me to provide feedback and advice. Describe your progress so far and your plan to finish. Keep it brief and to the point: enough for me to understand what you are doing, assuming the material covered in class as background knowledge. Hopefully, you can reuse most of pieces the milestone report as a part of the final report.

Final report

Final project reports should be emailed as PDF attachments to me by noon on April 15, 2010. The reports should be at most 6 pages. The format of your report should resemble a conference paper, with a general outline of the form:

Abstract
Introduction/Motivation
Methods/Algorithms
Results/Experiments
Discussion/Conclusion
References

The projects will be evaluated according to the following four criteria:

Soundness: Are the claims technically correct and techniques and approaches reasonable for the problem?
Significance: Is the problem addressed important and/or interesting?
Novelty: Is there something new and interesting about the project (novel application, algorithm, analysis, evaluation)?
Clarity: Is the presentation clear and concise, but complete enough for someone familiar with graphical models and machine learning?