CSCI 544 — Applied Natural Language Processing Coding Exercise 1


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CSCI 544 — Applied Natural Language
Coding Exercise 1

This assignment counts for 9% of the course grade.
Assignments turned in after the deadline but before February 10 are subject to a 30% grade
In this assignment you will write a naive Bayes classifier to identify hotel reviews as either
truthful or deceptive, and either positive or negative. You will be using the word tokens as
features for classification. 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.
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 files:
A top-level directory with two sub-directories, one for positive reviews and another for
negative reviews (plus license and readme files which you won’t need for the exercise).
Each of the subdirectories contains two sub-directories, one with truthful reviews and
one with deceptive reviews.
Each of these subdirectories contains four subdirectories, called “folds”.
Each of the folds contains 80 text files with English text (one review per file).
The grading script will train your model on all of the training data, and test the model on
unseen data in a similar format. The directory structure and file names of the test data will
be masked so that they do not reveal the labels of the individual test files.
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You will write two programs: will learn a naive Bayes model from the training
data, and will use the model to classify new data. If using Python 3, you will
name your programs and The learning program will be
invoked in the following way:
> python /path/to/input
The argument is the directory of the training data; the program will learn a naive Bayes
model, and write the model parameters to a file called nbmodel.txt. The format of the
model is up to you, but it should follow the following guidelines:
1. The model file should contain sufficient information for to successfully
label new data.
2. The model file 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 /path/to/input
The argument is the directory of the test data; the program will read the parameters of a
naive Bayes model from the file nbmodel.txt, classify each file in the test data, and write
the results to a text file called nboutput.txt in the following format:
label_a label_b path1
label_a label_b path2

In the above format, label_a is either “truthful” or “deceptive”, label_b is either “positive”
or “negative”, and pathn is the path of the text file being classified.
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 is invoked. The submission script uses a specific portion of the
training data as development data; it trains your model on the remaining 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 should only upload your program files to Vocareum, that is and (plus any required auxiliary files, such as code shared
between the programs or a word list that you wrote yourself).
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.
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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 the TAs.
After the due date, we will train your model on the full training data (including development
data), run your classifier on unseen test data, and compute the F1 score of your output
compared to a reference annotation for each of the four classes (truthful, deceptive, 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 naive Bayes
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).
Development data. While developing your programs, you should reserve some of the
data as development data in order to test the performance of your programs. The
submission script on Vocareum will use folds 2, 3, and 4 as training data, and fold 1 as
development data: that is, it will run on a directory containing only folds
2, 3, and 4, and it will run on a directory with a modified version of
fold 1, where directory and file names are masked. While developing on your own you
may use different splits of the data (but to get the same results as the submission
script, you’ll need to use the same split). The grading script will use all 4 folds for
training, and unseen data for testing.
Problem formulation. You may treat the problem as two binary classification problems
(truthful/deceptive and positive/negative), or as a 4-class single classification problem.
Choose whichever works better.
Smoothing and unknown tokens. You should implement some method of smoothing
for the training data and a way to handle unknown vocabulary in the test data,
otherwise your programs won’t work. For example, you can use add-one smoothing on
the training data, and simply ignore unknown tokens in the test data. You may use
more sophisticated methods which you implement yourselves.
Tokenization. You’d need to develop some reasonable method of identifying tokens in
the text (since these are the features for the naive Bayes classifier). 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.
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.
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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
You may use packages in the Python Standard Library. You may not use any other
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
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.

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