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

An N-gram is a contiguous sequence of N items from a given text or speech. For example, a 'bi-gram' is a 2-gram, and a 'tri-gram' is a 3-gram. The question asks for the value of N in the context of an 'N-gram', and among the given options, 9 is presented as the correct numerical value for N.

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

To determine the number of trigrams from the second sentence, first, the text is cleaned by replacing punctuation with spaces and removing stopwords ('is', 'a', 'to'). This results in the sequence: 'Analytics vidhya great source learn data science' (7 words). A trigram is a sequence of three consecutive words, so the number of trigrams is calculated as (number of words - 2), which is 7 - 2 = 5.

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

The dates in the text are 'September 21' and 'March 31, 2016'. The provided regular expressions are designed to match dates in the `YYYY-MM-DD` format. Since neither of the dates in the text follows this specific format, none of the given regular expressions would successfully extract them.

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

While Support Vector Machines (SVM) and Naive Bayes are general-purpose classification algorithms often used for text, modern sentiment analysis on tweet data, especially for nuanced categories like positive, negative, and neutral, frequently leverages more advanced techniques. These often include deep learning models like Recurrent Neural Networks (RNNs) or Transformer-based models, which are better equipped to capture complex contextual patterns and semantic relationships in short, informal text like tweets. Therefore, neither SVM nor Naive Bayes might be considered the *best* or *most direct* solution in a contemporary data science context for this specific problem.

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

Removing stopwords reduces the number of unique terms, directly lowering the dimensionality of the document-term matrix. Similarly, word normalization techniques like stemming or lemmatization consolidate different forms of a word into a single base form, which also decreases the total number of unique terms and thus the data's dimensionality. Converting words to lowercase also reduces dimensionality by treating 'Word' and 'word' as the same term, making statement 3 false.

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Practice Test

Free Master of Data Science Natural Language Processing Questions and Answers