[SOLVED] Homework 2: text classification cs 4650/7650
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and binary labels for whether each movie was funny (+) or not funny (-). The data
are shown below: for example, the cell at the intersection of “Review 1” and “laugh”
indicates that the text of Review 1 contains 2 tokens of the word “laugh.”
Review laugh hilarious awesome dull yawn bland Y
1 2 1 1 1 1 0 +
2 0 1 2 0 0 0 +
3 3 0 0 0 0 1 +
4 0 1 0 2 1 0 –
5 1 1 1 2 0 2 –
6 1 0 0 2 2 0 –You may find it easier to complete this problem if you copy the data into a spreadsheet
and use formulas for calculations, rather than doing calculations by hand. Please report
all scores as log-probabilities, with 3 significant figures. [10 pts](a) Assume that you have trained a Naive Bayes model on data D to detect funny
vs. not funny movie reviews. Compute the model’s predicted score for funny and
not-funny to the following sentence S (i.e. P(+|S) and P(−|S)), and determine
which label the model will apply to S. [4 pts]S: “This film was hilarious! I didn’t yawn once. Not a single bland moment.
Every minute was a laugh.”
(b) The counts in the original data are sparse and may lead to overfitting, e.g. a strong
prior on assigning the “not funny” label to reviews that contain “yawn.” What
would happen if you applied smoothing? Apply add-1 smoothing and recompute
the Naive Bayes model’s predicted scores for S. Did the label change? [4 pts]
(c) What is an additional feature that you could extract from text to improve the
classification of sentences like S, and how would it help improve the classification?
[2 pt]2. [CS 7650 Only]
Assume that you are training several logistic regression models. After training on the
same data, ˆθ is the optimal weight for an unregularized logistic regression model and
θ
∗
is the optimal weight for a logistic regression model with L2 regularization. Prove
that ||θ
∗
||2
2 ≤ ||ˆθ||2
2
.
Note: you may find it useful to look at the likelihood equations for regularized and
unregularized logistic regression. [5 pts]3. Language Modeling is the technique that allows us to compute the probabilities of
word sequences. The probability of a sequence W = w
n
1 = {w1, w2…wn}, with the use
of chain rule, can be estimated as the product of probabilities of each word given the
history, as shownP(W) = P(w1, w2…wn)
= P(w1) P(w2|w1) P(w3|w1, w2)…P(wn|w1, w2…wn−1)
=
Yn
i=1
P(wi
|w
i−1
1
)(a) Using an n-gram model allows us to approximate the above probability using
only a subset of of n − 1 words from the history at each step. Simplify the above
expression for the general n-gram case, and the bi-gram case. [3 pts](b) A common way to have markers for the start and the end of sentence is to add
the [BOS] (beginning of sentence) and [EOS] (end of sentence) tokens at the start
and end of every sentence. Consider the following text snippet-
[BOS] i made cheese at home [EOS]
[BOS] i like home made cheese [EOS]
[BOS] cheese made at home is tasty [EOS]
[BOS] i like cheese that is salty [EOS]Using the expression derived in (a), find the probability of the following sequence
as per the bi-gram model- P([BOS] I like cheese made at home [EOS]). [5 pts](c) In practice, instead of raw probability, perplexity is used as the metric for evaluating
a language model. Define perplexity and find the value of perplexity for the
sequence in (b) for the bi-gram case. [2 pts](d) One way to deal with unseen word arrangements in the test set is to use Laplace
smoothing, which adds 1 to all bi-gram counts, before we normalize them into
probabilities. An alternative to Laplace smoothing (add-1 smoothing) is add-k
smoothing, where k is a fraction that allows assigning a lesser probability mass
to unseen word arrangements. Find the probability of the sequence in (b) with
add-k smoothing for k = 0.1. [5 pts](e) To deal with unseen words in the test set, a common way is to fix a vocabulary
by thresholding on the frequency of words, and assigning an [UNK] token to
represent all out-of-vocabulary words. In the example from (a), use a threshold of
count > 1 to fix the vocabulary. Find the probability for the following sequence for
an add-0.1 smoothed bi-gram model- P([BOS] i like pepperjack cheese [EOS]). [5
pts]4. In this problem, you will do text classifications for Hate Speech. You need both answer
the questions and submit your codes.
Hate speech is a
(a) deliberate attack,
(b) directed towards a specific group of people,
(c) motivated by aspects of the group’s identity.The three premises must be true for a sentence to be categorized as HATE. Here are
two examples:
(a) “Poor white kids being forced to treat apes and parasites as their equals.”
(b) “Islam is a false religion however unlike some other false religions it is crude and
appeals to crude people such as arabs.”In (a), the speaker uses “apes” and “parasites” to refer to children of dark skin and
implies they are not equal to “white kids”. That is, it is an attack to the group
composed of children of dark skin based on an identifying characteristic, namely, their
skin colour. Thus, all the premises are true and (a) is a valid example of HATE.
Example (b) brands all people of Arab origin as crude. That is, it attacks the group
composed of Arab people based on their origin.Thus, all the premises are true and
(b) is a valid example of HATE.
This problem will require programming in Python 3. The goal is to build a Naive
Bayes model and a logistic regression model that you learnt from the class on a
real-world hate speech classification dataset. Finally, you will explore how to design
better features and improve the accuracy of your models for this task.The dataset you will be using is collected from Twitter online. Each example is labeled
as 1 (hatespeech) or 0 (Non-hatespeech). To get started, you should first download the
data and starter code from https://www.cc.gatech.edu/classes/AY2020/cs7650_
spring/programming/h2_text_classification.zip. Try to run:
python main.py — model AlwaysPredictZeroThis will load the data and run a default classifier AlwaysPredictZero which always
predicts label 0 (non-hatespeech). You should be able to see the reported train accuracy
= 0.4997. That says, always predicting non-hatespeech isn’t that good. Let’s try to
build better classifiers!Note that you need to implement models without using any machine learning packages
such as sklearn. We will only provide train set, and we will evaluate your code based
on our test set.To have a quick check with your implementations, you can randomly split the dataset
we give you into train and test set at a ration 8:2, compare the accuracy between the
models you have implemented and related models in sklearn packages. You would
expect an accuracy at around 0.65 (or above) on your test set.(a) (Naive Bayes) In this part, you should implement a Naive Bayes model with
add-1 smoothing, as we taught in the class. You are required to implement the
NaiveBayesClassifier class in classifiers.py. You would probably want to
take a look at the UnigramFeature class in utils.py that we have implemented
for you already. After you finish your codes, run python main.py –model
NaiveBayes to check the performance. List the 10 words that, under your model,
have the higest ratio of P(w|1)
P(w|0) (the most distinctly hatespeech words). List the 10
words with the lowest ratio. What trends do you see? [25 pts](b) (Logistic Regression) In this part, you should implement a Logistic Regression
model. You are required to implement the LogisticRegressionClassifier
class in classifiers.py. First, implement a logistic regression model without
regularization and run python main.py –model LogisticRegression, compare
the performance with your Naive Bayes approach. Next, we would like to experiment
with L2 regularization, add L2 regularization with different weight such as α =
{0.0001, 0.001, 0.01, 0.1, 1, 10}, describe what you observed. (You may want to
split the train set we give you into your own train and test set to observe the
performance) [25 pts](c) (Features) In the last part, you’ll explore and implement a more sophisicated set
of features. You need to implement the class BigramFeature or modify the class
CustomFeature inutils.py. Here are some common strategies (you are welcome
to implement some of them but try to come up with more!):
i. Remove stopwords (e.g. a, the, in),
ii. Use a mixture of unigrams, bigrams or trigrams,
iii. Use TF-IDF (refer to http://www.tfidf.com/) features.Use your creativity for this problem and try to obtain an accuracy as high as
possible on your test set! After you implement CustomFeature , run:
python main.py –model NaiveBayes — feature customized
python main.py –model LogisticRegression — feature customized
Describe the features that you have implemented. We’ll evaluate your two models
on the test set. [Bonus: 10 points]You will receive up to 10 bonus points: up to 5 points based on the novel features
you try and the rest based on how well your models perform compared to othersubmissions:
Bonus = 5 + 5 ∗
1
rank
e.g. if you rank first in the class, you will receive the full bonus point! We will
share the winners’ codes as well.