FREE Master of Data Science Natural Language Processing Questions and Answers

Question 1

The combination of N keywords is known as a "N-gram."

Accepted Answer

9

Answer

10

Answer

8

Answer

7

Question 2

How many bi-grams can be made out of the following sentence: "Analytics Vidhya is an excellent resource to study data science."
How many trigrams can be made from the following sentence using the techniques below for text cleaning:
Stopword Removal
Replacing punctuations by a single space
“#Analytics-vidhya is a great source to learn @data science.

Accepted Answer

5

Answer

7

Answer

6

Answer

4

Question 3

What regular expression from the list below may be used to find the date(s) in the text object? The following data science meetup will be place on September 21 of this year; the previous one was on March 31, 2016.

Accepted Answer

None of the above

Answer

(19|20)\d{2}-(0[1-9]|1[0-2])-[0-2][1-9]

Answer

(19|20)\d{2}-(0[1-9]|1[0-2])-([0-2][1-9]|3[0-1])

Answer

\d{4}-\d{2}-\d{2}

Question 4

You have amassed a dataset of roughly 10,000 rows of tweet text alone. You want to develop a model for classifying tweets that divides each one into three categories: positive, negative, and neutral. Which of the following models can classify tweets in light of the aforementioned context?

Accepted Answer

None of the above

Answer

SVM

Answer

Naive Bayes

Question 5

You treated each tweet as a separate document when creating a document term matrix from the data. Which of the following statements about the document term matrix is true?
1.Removal of stopwords from the data will affect the dimensionality of data
2.Normalization of words in the data will reduce the dimensionality of data
3.Converting all the words in lowercase will not affect the dimensionality of the data

Accepted Answer

1 and 2

Answer

Only 3

Answer

Only 2

Answer

Only 1

Question 6

Which of the following features can be utilized to enhance a classification model's accuracy?

Accepted Answer

All of these

Answer

Dependency Grammar

Answer

Frequency count of terms

Answer

Vector Notation of sentence

Question 7

What percentage of the total statements are correct 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\s

Accepted Answer

0

Answer

75

Answer

50

Answer

25

Question 8

What do alpha and beta hyperparameters in the Latent Dirichlet Allocation model for text categorization represent?

Accepted Answer

Alpha: density of topics generated within documents, beta: density of terms generated within topics True

Answer

Alpha: number of topics within documents, beta: number of terms within topics False

Answer

Alpha: number of topics within documents, beta: number of terms within topics False

Answer

Alpha: density of terms generated within topics, beta: density of topics generated within terms False

Question 9

What distinguishes the Conditional Random Field (CRF) from the Hidden Markov Model (HMM)?

Accepted Answer

CRF is Discriminative whereas HMM is Generative model

Answer

Both CRF and HMM are Discriminative model

Answer

Both CRF and HMM are Generative model

Answer

Dictionary based learning and WCRF is Generative whereas HMM is Discriminative modelord 2 vector model

Question 10

The two most utilized methods for creating chatbots are retrieval-based models and generative models. Which of the subsequent is a retrieval model example and a generating model illustration, respectively.

Accepted Answer

Rule-based learning and Sequence to Sequence model

Answer

Recurrent neural network and convolutional neural network

Answer

Word 2 vector and Sentence to Vector model

Answer

Dictionary based learning and Word 2 vector model