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COMS W4705 – Natural Language Processing – Homework 1

Total Points: 100
You are welcome to discuss the problems with other students but you must turn in your
own work. Please review the academic honesty policy for this course (at the end of the
syllabus page).
Create a .zip or .tgz archive containing any of the files you submit. Upload that single
file to Courseworks.
The file you submit should use the following naming convention:
YOURUNI_homework1.[zip | tgz]. For example, my uni is yb2235, so my file should be
named yb2235_homework1.zip or yb2235_whomework1.tgz.
As a reminder, any assignments submitted late will incur a 20 point penalty. No
submissions will be accepted later than 4 days after the submission deadline.
Analytical Component (30 pts)
Write up your solution in a single .pdf or plain (ASCII or UTF-8 encoded) .txt document.
Image files and Microsoft Word documents will not be accepted. If you must upload a
scan or photo converted into .pdf, make sure that the file size does not exceed 1MB.
Name your file written.txt or written.pdf and include it in the zip file you upload to
Courseworks.
Problem 1 (15 pts) – Text Classification with Naive Bayes
Consider the following training corpus of emails with the class labels ham and spam. The
content of each email has already been processed and is provided as a bag of words.
Email1 (spam): buy car Nigeria profit
Email2 (spam): money profit home bank
Email3 (spam): Nigeria bank check wire
Email4 (ham): money bank home car
Email5 (ham): home Nigeria fly
• Based on this data, estimate the prior probability for a random email to be spam or
ham if we don’t know anything about its content, i.e. P(Class)?
• Based on this data, estimate the conditional probability distributions for each word
given the class, i.e. P(Word | Class). You can write down these distributions in a
table.
• Using the Naive Bayes’ approach and your probability estimates, what is the
predicted class label for each of the following emails? Show your calculation.
o Nigeria
o Nigeria home
o home bank money
Problem 2 (15 pts) – Bigram Models
Show that, if you sum up the probabilities of all sentence of length n under a bigram
language model, this sum is exactly 1 (i.e. the model defines a proper probability
distribution). Assume a vocabulary size of V.
Hint: Use induction over the sentence length.
Comment: This property actually holds for any m-gram model, but you only have to
show it for bigrams.
Programming Component – Building a
Trigram Language Model (70 pts)
The instructions below are fairly specific and it is okay to deviate from implementation
details. However: You will be graded based on the functionality of each function.
Make sure the function signatures (function names, parameter and return
types/data structures) match exactly the description in this assignment.
Please make sure you are developing and running your code using Python 3.
Introduction
In this assignment you will build a trigram language model in Python.
You will complete the code provided in the file ‘trigram_model.py’. The main component
of the language model will be implemented in the class TrigramModel. Parts of this class
have already been provided for you and are explained below.
One important idea behind implementing language models is that the probability
distributions are not precomputed. Instead, the model only stores the raw counts of ngram occurrences and then computes the probabilities on demand. This makes
smoothing possible.
The data you will work with is available in a folder named ‘hw1_data’. There are two data
sets in this folder, which are described below in more detail.
Part 1 – extracting n-grams from a sentence (10 pts)
Complete the function get_ngrams, which takes a list of strings and an integer n as input,
and returns padded n-grams over the list of strings. The result should be a list of Python
tuples.
For example:
get_ngrams([“natural”,”language”,”processing”],1)
[(‘START’,), (‘natural’,), (‘language’,), (‘processing’,), (‘STOP’,)]
get_ngrams([“natural”,”language”,”processing”],2)
(‘START’, ‘natural’), (‘natural’, ‘language’), (‘language’, ‘processing’), (‘processi
ng’, ‘STOP’)]
get_ngrams([“natural”,”language”,”processing”],3)
[(‘START’, ‘START’, ‘natural’), (‘START’, ‘natural’, ‘language’), (‘natural’, ‘langua
ge’, ‘processing’), (‘language’, ‘processing’, ‘STOP’)]
Part 2 – counting n-grams in a corpus (10 pts)
We will work with two different data sets. The first data set is the Brown corpus, which is
a sample of American written English collected in the 1950s. The format of the data is a
plain text file brown_train.txt, containing one sentence per line. Each sentence has
already been tokenized. For this assignment, no further preprocessing is necessary.
Don’t touch brown_test.txt yet. We will use this data to compute the perplexity of our
language model.
Reading the Corpus and Dealing with Unseen Words
This part has been implemented for you and are explained in this section. Take a look at
the function corpus_reader in trigram_model.py. This function takes the name of a text
file as a parameter and returns a Python generator object. Generators allow you to iterate
over a collection, one item at a time without ever having to represent the entire data set
in a data structure (such as a list). This is a form of lazy evaluation. You could use this
function as follows:
generator = corpus_reader(“”)
for sentence in generator:
print(sentence)
[‘the’, ‘fulton’, ‘county’, ‘grand’, ‘jury’, ‘said’, ‘friday’, ‘an’, ‘investigation’,
‘of’, ‘atlanta’, “‘s”, ‘recent’, ‘primary’, ‘election’, ‘produced’, ‘“’, ‘no’, ‘evid
ence’, “””, ‘that’, ‘any’, ‘irregularities’, ‘took’, ‘place’, ‘.’]
[‘the’, ‘jury’, ‘further’, ‘said’, ‘in’, ‘term-end’, ‘presentments’, ‘that’, ‘the’, ‘
city’, ‘executive’, ‘committee’, ‘,’, ‘which’, ‘had’, ‘over-all’, ‘charge’, ‘of’, ‘th
e’, ‘election’, ‘,’, ‘“’, ‘deserves’, ‘the’, ‘praise’, ‘and’, ‘thanks’, ‘of’, ‘the’,
‘city’, ‘of’, ‘atlanta’, “””, ‘for’, ‘the’, ‘manner’, ‘in’, ‘which’, ‘the’, ‘electio
n’, ‘was’, ‘conducted’, ‘.’]
[‘the’, ‘september-october’, ‘term’, ‘jury’, ‘had’, ‘been’, ‘charged’, ‘by’, ‘fulton’
, ‘superior’, ‘court’, ‘judge’, ‘durwood’, ‘pye’, ‘to’, ‘investigate’, ‘reports’, ‘of
‘, ‘possible’, ‘“’, ‘irregularities’, “””, ‘in’, ‘the’, ‘hard-fought’, ‘primary’, ‘
which’, ‘was’, ‘won’, ‘by’, ‘mayor-nominate’, ‘ivan’, ‘allen’, ‘jr’, ‘&’, ‘.’]

Note that iterating over this generator object works only once. After you are done, you
need to create a new generator to do it again.
As discussed in class, there are two sources of data sparseness when working with
language models: Completely unseen words and unseen contexts. One way to deal with
unseen words is to use a pre-defined lexicon before we extract ngrams. The
function corpus_reader has an optional parameter lexicon, which should be a Python set
containing a list of tokens in the lexicon. All tokens that are not in the lexicon will be
replaced with a special “UNK” token.
Instead of pre-defining a lexicon, we collect one from the training corpus. This is the
purpose of the function get_lexicon(corpus). This function takes a corpus iterarator (as
returned by corpus_reader) as a parameter and returns a set of all words that appear in
the corpus more than once. The idea is that words that appear only once are so rare that
they are a good stand-in for words that have not been seen at all in unseen text. You do
not have to modify this function.
Now take a look at the __init__ method of TrigramModel (the constructor). When a new
TrigramModel is created, we pass in the filename of a corpus file. We then iterate through
the corpus twice: once to collect the lexicon, and once to count n-grams. You will
implement the method to count n-grams in the next step.
Counting n-grams
Now it’s your turn again. In this step, you will implement the method count_ngramsthat
should count the occurrence frequencies for ngrams in the corpus. The method already
creates three instance variables of TrigramModel, which store the unigram, bigram, and
trigram counts in the corpus. Each variable is a dictionary (a hash map) that maps the
n-gram to its count in the corpus.
For example, after populating these dictionaries, we want to be able to query
model.trigramcounts[(‘START’,’START’,’the’)]
5478
model.bigramcounts[(‘START’,’the’)]
5478
model.unigramcounts[(‘the’,)]
61428
Where model is an instance of TrigramModel that has been trained on a corpus. Note
that the unigrams are represented as one-element tuples (indicated by the , in the end).
Note that the actual numbers might be slightly different depending on how you set things
up.
Part 3 – Raw n-gram probabilities (10 pts)
Write the methods raw_trigram_probability(trigram), raw_bigram_probability(bigram),
and
raw_unigram_probability(unigram).
Each of these methods should return an unsmoothed probability computed from the
trigram, bigram, and unigram counts. This part is easy, except that you also need to keep
track of the total number of words in order to compute the unigram probabilities.
Interlude – Generating text (OPTIONAL)
This part is a little trickier. Write the method generate_sentence, which should return a list
of strings, randomly generated from the raw trigram model. You need to keep track of
the previous two tokens in the sequence, starting with (“START”,”START”). Then, to
create the next word, look at all words that appeared in this context and get the raw
trigram probability for each.
Draw a random word from this distribution (think about how to do this — I will give hints
about how to draw a random value from a multinomial distribution on Piazza) and then
add it to the sequence. You should stop generating words once the “STOP” token is
generated. Here are some examples for how this method should behave:
model.generate_sentence()
[‘the’, ‘last’, ‘tread’, ‘,’, ‘mama’, ‘did’, ‘mention’, ‘to’, ‘the’, ‘opposing’, ‘sec
tor’, ‘of’, ‘our’, ‘natural’, ‘resources’, ‘.’, ‘STOP’]
model.generate_sentence()
[‘the’, ‘specific’, ‘group’, ‘which’, ’caused’, ‘this’, ‘to’, ‘fundamentals’, ‘and’,
‘each’, ‘berated’, ‘the’, ‘other’, ‘resident’, ‘.’, ‘STOP’]
The optional t parameter of the method specifies the maximum sequence length so that
no more tokens are generated if the “STOP” token is not reached before t words.
Part 4 – Smoothed probabilities (10 pts)
Write the method smoothed_trigram_probability(self, trigram) which uses linear
interpolation between the raw trigram, unigram, and bigram probabilities (see lecture for
how to compute this). Set the interpolation parameters to lambda1 = lambda2 =
lambda3 = 1/3. Use the raw probability methods defined before.
Part 5 – Computing Sentence Probability (10 pts)
Write the method sentence_logprob(sentence), which returns the log probability of an
entire sequence (see lecture how to compute this). Use the get_ngrams function to
compute trigrams and the smoothed_trigram_probability method to obtain probabilities.
Convert each probability into logspace using math.log2. For example:
math.log2(0.8)
-0.3219280948873623
Then, instead of multiplying probabilities, add the log probabilities. Regular probabilities
would quickly become too small, leading to numeric issues, so we typically work with log
probabilities instead.
Part 6 – Perplexity (10 pts)
Write the method perplexity(corpus), which should compute the perplexity of the model
on an entire corpus.
Corpus is a corpus iterator (as returned by the corpus_reader method).
Recall that the perplexity is defined as 2-l
, where l is defined as:
Here M is the total number of words. So to compute the perplexity, sum the log
probability for each sentence, and then divide by the total number of words in the
corpus.
Run the perplexity function on the test set for the Brown corpus brown_test.txt (see
main section at the bottom of the Python file for how to do this). The perplexity should
be less than 400. Also try computing the perplexity on the training data (which should be
a lot lower, unsurprisingly).
This is a form of intrinsic evaluation.
Part 7 – Using the Model for Text Classification (10 pts)
In this final part of the problem we will apply the trigram model to a text classification
task. We will use a data set of essays written by non-native speakers of English for the
ETS TOEFL test. These essays are scored according to skill level low, medium, or high.
We will only consider essays that have been scored as “high” or “low”. We will train a
different language model on a training set of each category and then use these models
to automatically score unseen essays. We compute the perplexity of each language
model on each essay. The model with the lower perplexity determines the class of the
essay.
The files ets_toefl_data/train_high.txt and ets_toefl_data/train_low.txt in the data
zip file contain the training data for high and low skill essays, respectively. The
directories ets_toefl_data/test_high and ets_toefl_data/test_low contain test essays
(one per file) of each category.
Complete the method essay_scoring_experiment. The method should be called by
passing two training text files, and two testing directories (containing text files of
individual essays). It returns the accuracy of the prediction.
The method already creates two trigram models, reads in the test essays from each
directory, and computes the perplexity for each essay. All you have to do is compare
the perplexities and the returns the accuracy (correct predictions / total predictions).
On the essay data set, you should easily get an accuracy of 80%.
Data use policy: Note that the ETS data set is proprietary and licensed to Columbia
University for research and educational use only (as part of the Linguistic Data
Consortium. This data set is extracted
from https://catalog.ldc.upenn.edu/LDC2014T06 (Links to an external site.)). You may
not use or share this data set for any other purpose than for this class.
What you need to submit
trigram_model.py
written.pdf or written.txt
Do not submit the data files.
Pack these files together in a .zip or .tgz file as described on top of this page.