Natural Language Processing (NLP) Multiple Choice Questions with answers 16

Top 5 MCQ on NLP, NLP quiz questions with answers, NLP MCQ questions, Solved questions in natural language processing, NLP practitioner exam questions, Add-1 smoothing, MLE, inverse document frequency

Multiple Choice Questions in NLP

 

1. Let us assume that we use the words ‘study’ ‘computer’ and ‘abroad’. It these are only informative words to classify that a mail is spam or not. Which of the following represent the maximum-likelihood estimate using add-one smoothing for P(study|spam)? Use the following table to answer the question;

‘study’	‘computer’	‘abroad’	Class
1 0 1 1 0 0 0 0 0 0	0 1 0 1 0 0 0 1 0 0	0 1 0 0 0 0 1 0 0 0	Not spam Not spam Not spam Not spam Spam Spam Spam Spam Spam Spam

a) 0/6

b) 0/8

c) 1/6

d) 1/8

Click here to view answer

Ans : (d)

Answer: (d) 1/8 Add-1 smoothing (Laplace smoothing) can be calculated using the following equation; For this question, count(study) under the class ‘Spam’ = 0, N = 6 (frequency of ‘spam’), and V = 2 (count of possible classes). Refer here for more on Add-1 smoothing.

 

2. What is the probability P (‘computer in abroad’ | spam) as per the data in the table given in question 1?

a) 1/6

b) 2/6

c) 1/36

d) 1/18

Click here to view answer

Ans : (c)

Answer: (c) 1/36 To estimate the probability of X given a class, we can use this formula as per Naïve bayes classifier; P(A1, A2, …, An |C) = P(A1| Cj) P(A2| Cj)… P(An| Cj) In our problem, P(computer|spam) = count(computer true under spam)/total spam = 1/6. P(abroad|spam) = count(abroad true under spam)/total spam = 1/6. P(‘computer in abroad’|spam) = P(computer|spam)*p(abroad|spam) = 1/6 * 1/6 = 1/36. Here, the word ‘in’ is ignored because as per the question it does not contribute well in classification.

 

3. What is the unsmoothed maximum likelihood estimate of P(Spam) for the data given in question 1?

a) 1

b) 6/10

c) 4/6

d) 3/5

Click here to view answer

Ans : (b)

Answer: (b) 6/10 and (d) 3/5 Unsmoothed MLE of P(Spam); P(Spam)         = (Number of time ‘Spam’ class appears in the dataset)/(Total occurrence                            of all classes)                         = 6/10 Unsmoothed denotes the use of raw data. For example, the count of certain words may be zero.

 

4. Which of the following increases the weight of rarely occurring terms in the document set?

a) Term frequency

b) Word frequency

c) Inverse document frequency

d) Bi-gram frequency

Click here to view answer

Ans : (c)

Answer: (c) Inverse document frequency (IDF) Inverse Document Frequency (IDF) It is a statistical weight used for measuring the importance of a term in a text document collection. It measures about how much information the word provides, i.e., if it's common or rare across all documents. IDF is calculated as follows; idft = log (N/dft) In this equation, N is the total number of documents in a collection and dft is the number of documents in which the term t appears. The IDF of a rare term is high, whereas the IDF of a frequent term is likely to be low. The idea would be to reduce the tf weight of a term by a factor that grows with its collection frequency in TF-IDF calculation. [For more, you can refer here Wikipedia]

 

5. The act of converting a text document into a set of individual words is referred as ______ .

a) Tokenization

b) Stemming

c) Lemmatization

d) All of the above

Click here to view answer

Ans : (a)

Answer: (a) Tokenization Tokenisation is the process of breaking up a given text into units called tokens. Tokens can be individual words, phrases or even whole sentences. Given a character sequence and a defined document unit, tokenization is the task of chopping it up into pieces, called tokens , perhaps at the same time throwing away certain characters, such as punctuation. For example, “How are you?” If the above sentence is tokenized, the result will consist of the following tokens; ‘How’, ‘are’, ‘you’.

 

*************

Related links:

• Go to Natural Language Processing home page

• Go to Natural Language Processing - Glossary

• Go to NLP - MCQ Quiz Home page

Top interview questions in NLP

NLP quiz questions with answers explained

Online NLP quiz with solutions

question and answers in natural language processing

unsmoothed maximum likelihood estimation

What is inverse document frequency

how inverse document frequency helps in tf-idf calculation

Top 5 important questions with answers in natural language processing

 

 

Explain add-1 (Laplace) smoothing with an example

Natural language processing keywords, what is add-1 smoothing, what is Laplace smoothing, explain add-1 smoothing with an example, unigram and bi-gram with add-1 laplace smoothing

Add-1 (Laplace) smoothing

We have used Maximum Likelihood Estimation (MLE) for training the parameters of an N-gram model. The problem with MLE is that it assigns zero probability to unknown (unseen) words. This is because, MLE uses a training corpus. If the word in the test set is not available in the training set, then the count of that particular word is zero and it leads to zero probability.

To eliminate this zero probability, we can do smoothing. Smoothing is about taking some probability mass from the events seen in training and assigns it to unseen events. Add-1 smoothing (also called as Laplace smoothing) is a simple smoothing technique that Add 1 to the count of all n-grams in the training set before normalizing into probabilities.

Example:

Recall that the unigram and bi-gram probabilities for a word w are calculated as follows;

P(w) = C(w)/N

P(w_n|w_n-1) = C(w_n-1 w_n)/C(w_n-1)

Where, P(w) is the unigram probability, P(w_n-1 w_n) is the bigram probability, C(w) is the count of occurrence of w in the training set, C(w_n-1 w_n) is the count of bigram (w_n-1 w_n) in the training set, N is the total number of word tokens in the training set.

Add-1 smoothing for unigrams

P_Laplace(w) = (C(w)+1)/N+|V|

Here, N is the total number of tokens in the training set and |V| is the size of the vocabulary represents the unique set of words in the training set.

As we have added 1 to the numerator, we have to normalize that by adding the count of unique words with the denominator in order to normalize.

Add-1 smoothing for bigrams

P_Laplace(w_n|w_n-1) = (C(w_n-1 w_n)+1)/C(w_n-1)+|V|

 

*************************

 

Related articles:

 

• Natural Language Processing - Home

• NLP Multiple Choice Questions - Home 

• NLP True/False Questions - Home

• NLP Glossary
  

 

Explain add-1 smoothing

What is laplace smoothing

How to apply laplace smoothing in NLP for smoothing

Unigram and bigram probability calculations with add-1 smoothing