CS 571 Natural Language Processing  Homework 1

CS 571 Natural Language Processing 
Homework 1
Note: Homework modified from Lisbon machine learning summer school.
1.1 Text Classification and Naive Bayes (15 points)
The goal of this assignment is for you to gain familiarity with the multinomial Naive Bayes classifier.
Specifically, you will look into an existing Python-based implementation and fill out the missing code block
to gain an understanding of applying multinomial Naive Bayes to text classification.
In the homework package (HW1.tar.gz), you are provided with the starter code and a dataset. The code
was written in Python 3.11 and numpy. If you would like to install this programming environment, please
go to https://www.continuum.io/downloads. Download the Anaconda version (with Python 3.11) that
is compatible with your operating system. The installation instructions are available at http://docs.
continuum.io/anaconda/install. Installing Anaconda will install both Python 3.11 and numpy.
There are two data files in the package: positive.review and negative.review. They correspond to
positive and negative book reviews. The text has been preprocessed so that each line contains a review
document; each token (e.g., year:2) represents a word and its frequency in the document. The last token
(e.g., #label#:negative) in each line indicates the polarity (label) of the document.
The starter code includes four files: linear classifier.py, multinomial naive bayes.py, run classifier.py,
sentiment reader.py. The functionality of the files should be self-evident.
• (10 points) The file multinomial naive bayes.py currently has a missing code block. Search TODO
in the file and you will find the missing block. Your task is to fill out the missing code. Upon successful
completion of the code, you will run python run classifier.py and this will return the following
results: Accuracy on training set: 0.972500, on test set: 0.835000.
• (5 points) The starter code randomly chooses about 80% the documents to form the training set and
the rest as test set. Modify the code so that the train/test split is 50%/50%. After that, run python
run classifier.py and report the returned results.
Please submit: A report named report firstname lastname.pdf. Copy and paste to the report: 1) the
missing code block you filled in, and 2) the returned result after modifying the train/test splits.
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