NLP301c Full Final Exam
Which of the following techniques can be used for the C
purpose of keyword normalization, the process of
converting a keyword into its base form?
Lemmatization
Levenshtein
Stemming
Soundex
A) 1 and 2
B) 2 and 4
C) 1 and 3
D) 1, 2 and 3
E) 2, 3 and 4
F) 1, 2, 3 and 4
N-grams are defined as the combination of N keywords C
together. How many bi-grams can be generated from a
given sentence:
"Analytics Vidhya is a great source to learn data
science"
A) 7
B) 8
C) 9
D) 10
E) 11
How many trigrams phrases can be generated from the C
following sentence, after performing following text
cleaning steps:
Stopword Removal
Replacing punctuations by a single space
"#Analytics-vidhya is a great source to learn
@data_science."
A) 3
B) 4
C) 5
D) 6
E) 7
Which of the following regular expression can be used D
to identify date(s) present in the text object:
"The next meetup on data science will be held on 2017-
09-21, previously it happened on 31/03, 2016"
A) \d{4}-\d{2}-\d{2}
B) (19|20)\d{2}-(0[1-9]|1[0-2])-[0-2][1-9]
C) (19|20)\d{2}-(0[1-9]|1[0-2])-([0-2][1-9]|3[0-1])
D) None of the above
, NLP301c Full Final Exam
Which of the following models can perform tweet C
classification with regards to context mentioned
above?
A) Naive Bayes
B) SVM
C) None of the above
You have created a document term matrix of the data, D
treating every tweet as one document. Which of the
following is correct, in regards to document term
matrix?
Removal of stopwords from the data will affect the
dimensionality of data
Normalization of words in the data will reduce the
dimensionality of data
Converting all the words in lowercase will not affect the
dimensionality of the data
A) Only 1
B) Only 2
C) Only 3
D) 1 and 2
E) 2 and 3
F) 1, 2 and 3
Which of the following features can be used for E
accuracy improvement of a classification model?
A) Frequency count of terms
B) Vector Notation of sentence
C) Part of Speech Tag
D) Dependency Grammar
E) All of these
What percentage of the total statements are correct A
with regards to Topic Modeling?
It is a supervised learning technique
LDA (Linear Discriminant Analysis) can be used to
perform topic modeling
Selection of number of topics in a model does not
depend on the size of data
Number of topic terms are directly proportional to size
of the data
A) 0
B) 25
C) 50
D) 75
E) 100
, NLP301c Full Final Exam
In Latent Dirichlet Allocation model for text D
classification purposes, what does alpha and beta
hyperparameter represent-
A) Alpha: number of topics within documents, beta:
number of terms within topics
B) Alpha: density of terms generated within topics, beta:
density of topics generated within terms
C) Alpha: number of topics within documents, beta:
number of terms within topics
D) Alpha: density of topics generated within
documents, beta: density of terms generated within
topics
Solve the equation according to the sentence "I am D
planning to visit New Delhi to attend Analytics Vidhya
Delhi Hackathon".
A = (# of words with Noun as the part of speech tag)B =
(# of words with Verb as the part of speech tag)C = (#
of words with frequency count greater than one)
What are the correct values of A, B, and C?
A) 5, 5, 2
B) 5, 5, 0
C) 7, 5, 1
D) 7, 4, 2
E) 6, 4, 3
In a corpus of N documents, one document is randomly B
picked. The document contains a total of T terms and
the term "data" appears K times.
What is the correct value for the product of TF (term
frequency) and IDF (inverse-document-frequency), if
the term "data" appears in approximately one-third of
the total documents?
A) KT * Log(3)
B) K * Log(3) / T
C) T * Log(3) / K
D) Log(3) / KT
Which of the following technique is not a part of D
flexible text matching?
A) Soundex
B) Metaphone
C) Edit Distance
D) Keyword Hashing
, NLP301c Full Final Exam
True or False: Word2Vec model is a machine learning B
model used to create vector notations of text objects.
Word2vec contains multiple deep neural networks
A) TRUE
B) FALSE
Which of the following statement is(are) true for C
Word2Vec model?
A) The architecture of word2vec consists of only two
layers - continuous bag of words and skip-gram model
B) Continuous bag of word (CBOW) is a Recurrent
Neural Network model
C) Both CBOW and Skip-gram are shallow neural
network models
D) All of the above
What is the right order for a text classification model C
components
Text cleaning
Text annotation
Gradient descent
Model tuning
Text to predictors
A) 12345
B) 13425
C) 12534
D) 13452
Polysemy is defined as the coexistence of multiple B
meanings for a word or phrase in a text object. Which of
the following models is likely the best choice to correct
this problem?
A) Random Forest Classifier
B) Convolutional Neural Networks
C) Gradient Boosting
D) All of these
Which of the following models can be used for the D
purpose of document similarity?
A) Training a word 2 vector model on the corpus that
learns context present in the document
B) Training a bag of words model that learns
occurrence of words in the document
C) Creating a document-term matrix and using cosine
similarity for each document
D) All of the above
Which of the following techniques can be used for the C
purpose of keyword normalization, the process of
converting a keyword into its base form?
Lemmatization
Levenshtein
Stemming
Soundex
A) 1 and 2
B) 2 and 4
C) 1 and 3
D) 1, 2 and 3
E) 2, 3 and 4
F) 1, 2, 3 and 4
N-grams are defined as the combination of N keywords C
together. How many bi-grams can be generated from a
given sentence:
"Analytics Vidhya is a great source to learn data
science"
A) 7
B) 8
C) 9
D) 10
E) 11
How many trigrams phrases can be generated from the C
following sentence, after performing following text
cleaning steps:
Stopword Removal
Replacing punctuations by a single space
"#Analytics-vidhya is a great source to learn
@data_science."
A) 3
B) 4
C) 5
D) 6
E) 7
Which of the following regular expression can be used D
to identify date(s) present in the text object:
"The next meetup on data science will be held on 2017-
09-21, previously it happened on 31/03, 2016"
A) \d{4}-\d{2}-\d{2}
B) (19|20)\d{2}-(0[1-9]|1[0-2])-[0-2][1-9]
C) (19|20)\d{2}-(0[1-9]|1[0-2])-([0-2][1-9]|3[0-1])
D) None of the above
, NLP301c Full Final Exam
Which of the following models can perform tweet C
classification with regards to context mentioned
above?
A) Naive Bayes
B) SVM
C) None of the above
You have created a document term matrix of the data, D
treating every tweet as one document. Which of the
following is correct, in regards to document term
matrix?
Removal of stopwords from the data will affect the
dimensionality of data
Normalization of words in the data will reduce the
dimensionality of data
Converting all the words in lowercase will not affect the
dimensionality of the data
A) Only 1
B) Only 2
C) Only 3
D) 1 and 2
E) 2 and 3
F) 1, 2 and 3
Which of the following features can be used for E
accuracy improvement of a classification model?
A) Frequency count of terms
B) Vector Notation of sentence
C) Part of Speech Tag
D) Dependency Grammar
E) All of these
What percentage of the total statements are correct A
with regards to Topic Modeling?
It is a supervised learning technique
LDA (Linear Discriminant Analysis) can be used to
perform topic modeling
Selection of number of topics in a model does not
depend on the size of data
Number of topic terms are directly proportional to size
of the data
A) 0
B) 25
C) 50
D) 75
E) 100
, NLP301c Full Final Exam
In Latent Dirichlet Allocation model for text D
classification purposes, what does alpha and beta
hyperparameter represent-
A) Alpha: number of topics within documents, beta:
number of terms within topics
B) Alpha: density of terms generated within topics, beta:
density of topics generated within terms
C) Alpha: number of topics within documents, beta:
number of terms within topics
D) Alpha: density of topics generated within
documents, beta: density of terms generated within
topics
Solve the equation according to the sentence "I am D
planning to visit New Delhi to attend Analytics Vidhya
Delhi Hackathon".
A = (# of words with Noun as the part of speech tag)B =
(# of words with Verb as the part of speech tag)C = (#
of words with frequency count greater than one)
What are the correct values of A, B, and C?
A) 5, 5, 2
B) 5, 5, 0
C) 7, 5, 1
D) 7, 4, 2
E) 6, 4, 3
In a corpus of N documents, one document is randomly B
picked. The document contains a total of T terms and
the term "data" appears K times.
What is the correct value for the product of TF (term
frequency) and IDF (inverse-document-frequency), if
the term "data" appears in approximately one-third of
the total documents?
A) KT * Log(3)
B) K * Log(3) / T
C) T * Log(3) / K
D) Log(3) / KT
Which of the following technique is not a part of D
flexible text matching?
A) Soundex
B) Metaphone
C) Edit Distance
D) Keyword Hashing
, NLP301c Full Final Exam
True or False: Word2Vec model is a machine learning B
model used to create vector notations of text objects.
Word2vec contains multiple deep neural networks
A) TRUE
B) FALSE
Which of the following statement is(are) true for C
Word2Vec model?
A) The architecture of word2vec consists of only two
layers - continuous bag of words and skip-gram model
B) Continuous bag of word (CBOW) is a Recurrent
Neural Network model
C) Both CBOW and Skip-gram are shallow neural
network models
D) All of the above
What is the right order for a text classification model C
components
Text cleaning
Text annotation
Gradient descent
Model tuning
Text to predictors
A) 12345
B) 13425
C) 12534
D) 13452
Polysemy is defined as the coexistence of multiple B
meanings for a word or phrase in a text object. Which of
the following models is likely the best choice to correct
this problem?
A) Random Forest Classifier
B) Convolutional Neural Networks
C) Gradient Boosting
D) All of these
Which of the following models can be used for the D
purpose of document similarity?
A) Training a word 2 vector model on the corpus that
learns context present in the document
B) Training a bag of words model that learns
occurrence of words in the document
C) Creating a document-term matrix and using cosine
similarity for each document
D) All of the above
