CS 5761 - Introduction to Natural Language Processing

Project Beta version due by 5pm Thursday, April 29. Submit a tar file with your system, and a pdf/doc file with your updated proposal/report via webdrop. Demo in lab at 6pm that same day.


To produce a beta version of your project, where you have implemented the majority of all of your system's functionality, and have updated your proposal such that it will be suitable as the core of your final report.

System Requirements

You should turn in a tar file that unpacks a directory named with your user-id, and the course number. For example, in my case this would be tpederse-5761. Your tar file should include all of your system code and data necessary for running your system and for evaluation.

Make sure you provide the following in your tar file: A useful hint: Have a friend do a test installation of your code to make sure it can be easily installed and run. You will be surprised how many things you take for granted in using your own code that are not apparent to someone else.

Anyone who uses your tar file should be able to unpack the code, look at the README, and then run the the system itself within just a few minutes. There should be very few demands placed on the user in order to figure out how to run your project code. Part of the grade of your final system submission will be determined based on whether or not we can install and run and evaluate your system quickly. Again, make sure to test on a csdev machine as this is the platform we'll use for testing.

For the Voynich projects, make sure you provide the transcribed version of the manuscript as a part of your project tar file. For the Google Sets project, you may assume that I have WordNet already available, so you don't need to provide that.

Proposal/Report Requirements

This version of your proposal (now morphing into a final report) should contain all of the changes that I have mentioned in my comments of April 9 to the class, as well as any comments that have been made on either your initial proposal or the alpha version. You should pay particular attention to providing full details of your system's approach, as well as a detailed description of how you will do evaluation.

Finally, remember that you want your final report to be a document that someone who was not a part of this class could read and understand. So please make sure you provide sufficient background regarding your problem, and explain what you have done clearly and without making any assumptions that the reader will be familiar with either Google Sets or the Voynich Manuscript.