CSCI 544 — Applied Natural Language Processing

Coding Exercise 3

Due: April 26, 2018, at 23:59 Pacific Time (11:59 PM)

This assignment counts for 20% of the course grade.

Assignments turned in after the deadline but before April 29 are subject to a 30% grade penalty.

Overview

In this assignment you will write perceptron classifiers (vanilla and averaged) to identify hotel reviews as either true or fake, and either positive or negative. You may using the word tokens as features, or any other features you can devise from the text. The assignment will be graded based on the performance of your classifiers, that is how well they perform on unseen test data compared to the performance of a reference classifier.

Data

The training and development data are the same as for Coding Exercise 2, and are available as a compressed ZIP archive on Blackboard. The uncompressed archive contains the following files:

• One file train-labeled.txt containing labeled training data with a single training instance (hotel review) per line (total 960 lines). The first 3 tokens in each line are:
  1. a unique 7-character alphanumeric identifier
  2. a label True or Fake
  3. a label Pos or Neg
  These are followed by the text of the review.
• One file dev-text.txt with unlabeled development data, containing just the unique identifier followed by the text of the review (total 320 lines).
• One file dev-key.txt with the corresponding labels for the development data, to serve as an answer key.
• Readme and license files (which you won’t need for the exercise).

Programs

The perceptron algorithms appear in Hal Daumé III, A Course in Machine Learning (v. 0.99 draft), Chapter 4: The Perceptron.

You will write two programs: perceplearn.py will learn perceptron models (vanilla and averaged) from the training data, and percepclassify.py will use the models to classify new data. If using Python 3, you will name your programs perceplearn3.py and percepclassify3.py. The learning program will be invoked in the following way:

> python perceplearn.py /path/to/input

The argument is a single file containing the training data; the program will learn perceptron models, and write the model parameters to two files: vanillamodel.txt for the vanilla perceptron, and averagedmodel.txt for the averaged perceptron. The format of the model files is up to you, but they should follow the following guidelines:

1. The model files should contain sufficient information for percepclassify.py to successfully label new data.
2. The model files should be human-readable, so that model parameters can be easily understood by visual inspection of the file.

The classification program will be invoked in the following way:

> python percepclassify.py /path/to/model /path/to/input

The first argument is the path to the model file (vanillamodel.txt or averagedmodel.txt), and the second argument is the path to a file containing the test data file; the program will read the parameters of a perceptron model from the model file, classify each entry in the test data, and write the results to a text file called percepoutput.txt in the same format as the answer key.

Submission

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

Multiple submissions are allowed; only the final submission will be graded. Each time you submit, a submission script trains your model on the training data, runs your classifier on the development data, and reports the results. Do not include the data in your submission: the submission script reads the data from a central directory, not from your personal directory. 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 performance of you classifier will be measured automatically; failure to format your output correctly may result in very low scores, which will not be changed.

For full credit, make sure to submit your assignment well before the deadline. The time of submission recorded by the system is the time used for determining late penalties. If your submission is received late, whatever the reason (including equipment failure and network latencies or outages), it will incur a late penalty.

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

Grading

After the due date, we will train your model on a combination of the training and development data, run your classifier on unseen test data twice (once with the vanilla model, and once with the averaged model), and compute the F1 score of your output for each of the four classes (true, fake, positive, and negative). Your grade will be based on the performance of your classifier. We will calculate the mean of the four F1 scores and scale it to the performance of a perceptron classifier developed by the instructional staff (so if that classifier has F1=0.8, then a score of 0.8 will receive a full credit, and a score of 0.72 will receive 90% credit; your vanilla perceptron will be compared to a reference vanilla perceptron, and your averaged perceptron will be compared to a reference averaged perceptron).

Notes

• Problem formulation. Since a perceptron is a binary classifier, you need to treat the problem as two separate binary classification problems (true/fake and positive/negative); each of the model files (vanilla and averaged) needs to have the model parameters for both classifiers.
• Features and tokenization. You’d need to develop some reasonable method of identifying features in the text. Some common options are removing certain punctuation, or lowercasing all the letters. You may also find it useful to ignore certain high-frequency or low-frequency tokens. You may use any tokenization method which you implement yourselves. Experiment, and choose whichever works best.
• Runtime efficiency. Vocareum imposes a limit on running times, and if a program takes too long, Vocareum will kill the process. Your program therefore needs to run efficiently. You need an efficient way to store the training instances, in order to avoid reading them over and over again (reading and parsing text is slow). Also, feature vectors for individual training instances are typically fairly sparse: for a reference solution with about 1000 features, the mean number of non-zero features per training instance is about 77; it would be highly inefficient to multiply and add all the 900+ zeros at every step. The reference solution stores the training data as a python dict indexed by the unique identifiers of the reviews, and the feature vector for each training instance as a dict of the form feature:count. With about 1000 features and 100 iterations (which is far more than needed, due to overfitting), run times for the reference solution are under 5 seconds for running perceplearn.py on the training data, running on a MacBook Pro from 2016.
• Overfitting. The perceptron has a tendency to overfit the training data. For example, with about 1000 features, the reference solution models the training data perfectly after about 30 iterations.

Collaboration and external resources

• This is an individual assignment. You may not work in teams or collaborate with other students. You must be the sole author of 100% of the code you turn in.
• You may not look for solutions on the web, or use code you find online or anywhere else.
• You may not download the data from any source other than the files provided on Blackboard, and you may not attempt to locate the test data on the web or anywhere else.
• You may use packages in the Python Standard Library. You may not use any other packages.
• You may use external resources to learn basic functions of Python (such as reading and writing files, handling text strings, and basic math), but the extraction and computation of model parameters, as well as the use of these parameters for classification, must be your own work.
• Failure to follow the above rules is considered a violation of academic integrity, and is grounds for failure of the assignment, or in serious cases failure of the course.
• We use plagiarism detection software to identify similarities between student assignments, and between student assignments and known solutions on the web. Any attempt to fool plagiarism detection, for example the modification of code to reduce its similarity to the source, will result in an automatic failing grade for the course.
• Please discuss any issues you have on the Piazza discussion boards. Do not ask questions about the assignment by email; if we receive questions by email where the response could be helpful for the class, we will ask you to repost the question on the discussion boards.