CSCI 544 — Applied Natural Language Processing


Homework 6

Due: April 12, 2016, at 23:59 Pacific Time (11:59 PM)

Assignments turned in after the deadline but before April 15 are subject to a 30% grade penalty.


Overview

In this assignment you will write a Hidden Markov Model part-of-speech tagger for Catalan. The training data is provided tokenized and tagged; the test data will be provided tokenized, and your tagger will add the tags. The assignment will be graded based on the performance of your tagger, that is how well it performs on unseen test data compared to the performance of a reference tagger.

Data

A set of training and development data will be made available as a compressed ZIP archive on Blackboard. The uncompressed archive will have the following format:

The grading script will train your model on all of the tagged training and development data, and test the model on unseen data in a similar format.

Programs

You will write two programs: hmmlearn.py will learn a hidden Markov model from the training data, and hmmdecode.py will use the model to tag new data. If using Python 3, you will name your programs hmmlearn3.py and hmmdecode3.py. The learning program will be invoked in the following way:

> python hmmlearn.py /path/to/input

The argument is a single file containing the training data; the program will learn a hidden Markov model, and write the model parameters to a file called hmmmodel.txt. The format of the model is up to you, but it should contain sufficient information for hmmdecode.py to successfully tag new data.

The tagging program will be invoked in the following way:

> python hmmdecode.py /path/to/input

The argument is a single file containing the test data; the program will read the parameters of a hidden Markov model from the file hmmmodel.txt, tag each word in the test data, and write the results to a text file called hmmoutput.txt in the same format as the training data.

Grading

We will train your model, run your tagger on new test data, and compute the accuracy of your output compared to a reference annotation. Your grade will be the accuracy of your tagger, scaled to the performance of a reference HMM tagger developed by us. Since part-of-speech tagging can achieve high accuracy by using a baseline tagger that just gives the most common tag for each word, only the performance above the baseline will be scaled:

For example, if the baseline is 90%, the reference in 95%, and your accuracy is 93%, then your grade will be 0.9 + 0.03 × 0.1 / 0.05 = 96%.

Notes

Collaboration and external resources

Submission

All submissions will be completed through Vocareum; please consult the instructions for how to use Vocareum.

Multiple submissions are allowed, and your last submission will be graded. The submission script runs the program in a similar way to the grading script (but with different data), so you are encouraged to submit early and often in order to iron out any problems, especially issues with the format of the final output. The accuracy of you classifier will be measured automatically; failure to format your output correctly may result in very low scores, which will not be changed.

If you have any issues with Vocareum with regards to logging in, submission, code not executing properly, etc., please contact Siddharth.