Top 9 NLP Interview Questions for Beginners

Posted by December 23, 2020
Top 9 NLP Interview Questions for Beginners
Top 9 NLP Interview Questions for Beginners

Natural Language Processing or NLP helps machines analyze and understand natural language. In this automated process, machine learning algorithms are applied to data to help in extracting the required information. Before you sit for your interview process, it is important to know the kind of NLP interview questions that you may face. More often than not, applicants are confused regarding the kinds of NLP Interview questions that recruiters may ask. With the help of this blog, we will help you understand a few of the commonly asked questions for beginners. 

Top 9 NLP Interview Questions for Freshers

Question 1. Out of the following techniques, which one can be used for keyword normalization in NLP? 

  1. Lemmatization
  2. Soundex
  3. Cosine Similarity
  4. N-grams

Ans: Keyword normalization is the process of converting a keyword to its base form. The answer is option (a). Lemmatization allows us to get the base form of any word mentioned. For example, eating – eat, playing – play. 

Question 2. Out of the following, which technique is used for computing the distance between any two word vectors given in NLP? 

  1. Lemmatization
  2. Cosine Similarity 
  3. Euclidean distance
  4. N-grams

Ans: option (b) and option (c)

If you wish to calculate the distance between any two word vectors in NLP, you must use cosine similarity and euclidean distance. To establish a cosine angle between the vector, cosine similarity is used. If the cosine angle is close to each other, it indicates that the words are similar. And if they are not close to each other, it indicates that the words are not so similar. Let us take a look at an example. The cosine angle between two words that are related to sports, such as basketball and cricket, will be closer to 1 when compared to the angle between words such as basketball and Bangalore. 

Question 3. Which of the following are features of a text corpus in NLP?

  1. Vector notation of the word
  2. Count of the word in a document 
  3. Basic dependency grammar 
  4. Part of Speech Tag 

Ans: All of the above. All the features mentioned above can be used as the features of a text corpus in NLP.

Question 4. Out of the following, which can be a keyword normalization technique in NLP?

  1. Part of Speech
  2. Lemmatization
  3. Named entity recognition
  4. Stemming 

Ans. option (b) and option (d) 

Part of speech or POS and named entity recognition or NER cannot be considered as keyword normalization techniques in Natural Language Processing. NER can help you extract the time, date, city and other types of entities in a given sentence. POS allows you to extract the pronoun, adjective, verb, noun from the given sentence. 

Question 5. Out of the following, which of the following is a use case of NLP?

  1. Face Recognition
  2. Speech Biometric
  3. Detecting objects from a given image
  4. Text summarization 

Ans: option (d)

Face recognition and detecting objects from a given image are use cases of computer vision. Speech biometric is a use case of speech recognition. Only text summarization is a use case of NLP.

Question 6. Out of the given, which one is not a pre-processing technique in NLP?

  1. Converting to lowercase
  2. Stemming and Lemmatization
  3. Removing of stop words
  4. Removing punctuation 
  5. Sentiment analysis 

Ans: option (e)

Sentiment analysis is done after the pre-processing techniques and is actually a NLP use case. Thus, it is not a pre-process technique. The other options listed above are part of statement pre-processing.

Question 7. In this particular architecture, the relationship between all words in the given sentence is modeled irrespective of their position in the sentence. Name the architecture. 

  1. ELMo
  2. OpenAI GPT
  3. BERT
  4. UMLFit

Ans. option (c)

In the case of a BERT transformer architecture model, each word and all other words in the sentence have such a relationship that they can generate attention scores. An attention score is later used as the weight of a weighted average of all word representations. This attention score is fed to a fully-connected network and can be used to generate a new representation.  

Question 8: Name some of the use cases that can be solved with the help of NLP techniques.

Ans. Here are the 13 use cases that can be solve with the help of NLP techniques as mentioned below:-

  1. Language Translation (Chinese to English, English to German, and more)
  2. Document Summarization
  3. Sentiment Analysis
  4. Sentence Completion
  5. Question Answering 
  6. Topic Classification
  7. Intent extraction
  8. Document ranking
  9. Natural language inference
  10. Attribute extraction (extracting key information from documents)
  11. Chatbot interaction 
  12. Grammar or sentence correction 
  13. Image Captioning

Question 9. What do you understand by Bag of Words?

Ans. A commonly used model, Bag of words depends on the word frequencies or occurrence in order to train a classifier. A bag of words model creates an occurrence matrix. This means that all the documents or sentences irrespective of their grammar structure or word order occur. 

This brings us to the end of the blog on NLP Interview Questions. We hope that these NLP interview questions will help you prepare for your upcoming interview. Happy Learning!