Exam (elaborations)
Natural Language Processing Graded A+ Questions and Answers 2024
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Natural Language Processing Graded A+
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Natural Language Processing Graded A+Logistic Regression - answer An algebraic function that is used to relate any and all
independent variables to the expected dependent variable.
INPUT X ^(i) = column = [[1],[8],[11]]
HYPERPARAMTERS θ
sigmoid ( θ.T, X )
LABEL Y ex positive sentiment 1, Neg Sentiment =0
PREDECITED LABEL Y'
COST FUNCTION TO MINIMIZE L(Y,Y')
Gradient Descent = θ - alpha * gradient slope
vocabulary - answer List of unique words in a document
sentiment analysis - answer an automated process of analyzing and categorizing social
media to determine the amount of positive, negative, and neutral online comments a
brand receives
Looking at
Vocabulary you can create a
Positive Frequency
Negative frequency
associated to every word in vocabulary
sentiment analysis: Positive Frequency Dictionary - answer"I am happy because I am
learning NLP"
"I am Happy"
vocabulary:
I am happy because learning nlp sad not
33211100
Feature Extraction: Spare Representation - answerA representation that contains a lot
of zeros
example
vector of 1, 0's each representing the existence of the words in the vocabulary
CONS: Features are as large as the size of a Vocabulary. This could result in larger
training time, and large prediction time.
sentiment analysis: Negative Frequency Dictionary - answer"I am sad, I am not learning
NLP"
"I am Sad"
,vocabulary:
I am happy because learning nlp sad not
33001121
Feature Extraction: Frequencies of Words - answerwhere freq(word, sentiment class)
Xm = [ 1 (bias) , Σw freqs(w,1) pos.,Σw freqs(w,0) neg. ]
vocab: I am happy because learning nlp sad not
pos: 3 3 2 1 1 1 0 0
neg: 3 3 0 0 1 1 2 1
Σw = "I am sad, I am not learning NLP"
Σ freqs(w,1) pos.=I:3+am:3 +sad:0 +not:0+learn:1+NLP:1=8
Σ freqs(w,0) neg.=I:3+am:3 +sad:2 +not:1+learn:1+NLP:1=11
X shape (m, 3)
X1 = [ 1 , 3, 5]
X2 = [ 1 , 5, 4]
...
Xm = [ 1 , 8, 11 ] sample 1 row of m sample
Feature Extraction Preprocessing: Stop Words - answerFrequently used words that are
part of sentence but don't add value such as conjunctions and punctuations
Feature Extraction Preprocessing: Stemming - answerReducing Words to their base
derivations removing tense example
tuning -> tun
tune -> tun
tuned -> tun
reducing vocabulary size
Accuracy - answerΣi (pred^(i) == y^(i))/m
Sigmoid - answerprediction h(x,θ) vertical , θ.T (X) horizontal
Cost - answerusually graphed over iterations
-1/m Σi y^(i) log (h(x,θ)) + (1-y^(i)) log (1 - h(x,θ))
y= 1 and h(x,θ) = 0 infinity
y= 0 and h(x,θ) = 1 infinity
Probabilities - answerthe likelihood that something will happen
,Conditional Probability - answerthe probability that one event happens given that
another event is already known to have happened.
All the other givens are ignored
P(X|Y)
Bayes Rule - answerP(Positive|word) P(Word) = P( Positive ∩ word)
P(word|positive) P(Positive) = P( Positive ∩ word)
P(word|positive) P(Positive)=P(Positive|word) P(Word)
P(X|Y)= P(Y|X)P(X)/P(Y)
Naive Bayes Classifier for sentiment Analysis - answerpredicts the probability of a
certain outcome based on prior occurrences of related events
Assumes that Variables are Independent
For each Word in Vocabulary you can create a
Positive Frequency List
Negative frequency List
V = Count Total Words in Vocabulary
∀w Σw freqs(w,1) pos.
∀w Σw freqs(w,0) neg.
For Each word calculate new table
P(word | pos) = freqs(word,1) / ∀w Σw freqs(w,1) pos.
P(word | neg) = freqs(word,0) / ∀w Σw freqs(w,0) neg.
Σw P(w | pos) = 1
Σw P(w | neg) = 1
any word where P(w | pos) = P(w | neg) are neutral and don't add to sentiment
Also actively avoid P(w | pos)=0 or P(w | neg)=0
Power words are widely skewed
P(w | pos) >> P(w | neg) or P(w | pos) << P(w | neg)
1) Annotate tweets to be Pos or Neg
2) preprocess Tweets
Lowercase
Remove punctuation, urls, names
Remove stop words
Stemming
, Tokenize sentences
3) Get columns Σw freqs(w,1) pos. , Σw freqs(w,0) neg.
4) Get columns P(w | pos), P(w | neg)
5) Get column λ(w) = log (ratio(w))
6) calculate log( Prior Ratio)= log(P(pos)/P(neg))
Assumptions of Naïve Bayes - answerWords in a Sentence are assumed independent
Bad: Words can be used together to describe/reference another word in a sentence and
not necessarily be stand alone. example "sunny and hot" of "cold and snowy"
Relies on data distribution of training sets. Good training sets have equal frequencies of
data classifications.
Bias is present in sentiments of training tweets for example
Applications of Naïve Bayes - answerAuthor Identification
= P(author1 | book)/P(author2 | book)
Spam Filtering
= P(spam | email)/P(non spam | email)
Information Retrieval
P(document k |query) ~ Πi P( query i | document k)
retrieve relevant document in k documents if
P(document k |query) > threshold
Word disambiguation
= P(context 1 | ambig word)/P(context 2 | ambig word)
= P("RIVER" | "BANK")/P( "MONEY" | "BANK")
Ration Of Probabilities - answerratio(w) = P(w | pos)/P(w | neg)
POSITIVE = P(w | pos)/P(w | neg) => ∞
NEUTRAL = P(w | pos)/P(w | neg) = 1
NEGATIVE = P(w | pos)/P(w | neg) => 0
Prior Ratio - answerP(pos)/P(neg)
in a balance data sets P(pos)/P(neg)= 1
Naïve Bayes Inference condition Rule for Binary Classification - answersentence m= "I
am happy today; I am Learning"
Likelihood = Πm P(wm | pos)/P(wm | neg)
Naïve Bayes Inference= (Prior Ratio) (Likelihood )
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