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You have completed your NLP course and prepping to crack your Interview? Usually, applicants are not aware of the type of questions they may face in the interview. So, in this blog, we have compiled a list of the top 30 NLP interview questions that will help you along the way. You will find here the recent and most relevant basic to advanced interview questions on NLP 2023.

Natural Language Processing (NLP) is a method of using machine learning algorithms to identify and interpret the problem and overall meaning of natural language from spoken or written text.

NLP interview questions

Top 30 NLP Interview Questions and Answers

1. What are the five crucial components of Natural Language Processing (NLP)? Explain any two briefly.


The five crucial components of NLP are – Lexical Analysis, Syntactic Analysis, Semantic Analysis, Discourse Integration, and Pragmatic Analysis.

  • Lexical Analysis – is a process of identifying and analysing structures of words. It means dividing the chunk of text into words, sentences, and paragraphs.
  • Pragmatic Analysis – it deals with understanding and using in different situations. Deals with external word knowledge (document/queries).

2. What are some of the advantages of NLP?

The advantages of NLP are?

  • The users can ask questions concerning any subject and get a direct response in seconds.
  • It provides answers to the questions in natural language.
  • It enables computers to communicate with humans in their language and also scales other language-related tasks. 

3. What are some of the best NLP tools from an open source?

Some of the best NLP tools are:

  • TextBlop
  • SpaCy
  • Natural language toolkit (NLTK)
  • Stanford NLP

4. What do you understand by text extraction and cleanup in NLP?

The process of retrieving raw data from input data and removing all the non-textual information such as markup and metadata and thus converting the text to a specific encoding format. 

The following shows the methods used for text extraction in NLP:

  • Sentiment Analysis
  • Text summarization
  • Topic modelling
  • Named entity recognition

5. What do you mean by ‘Stop word’?

Stop words are words that are meaningless in search engines. For instance, stop words like was, in, the, a, how, at, with, etc act as connectors of sentences or phrases. The removal is for better understanding and analysing the meaning of a sentence. 

And engineers design the algorithm in such a way that stop words are not present, to get relevant search results. Hence, their removal is a priority.

6. What do you mean by NLTK?

NLTK stands for Natural Language Toolkit and is a Python library. NLTK is essential to process data in human-spoken languages. To understand natural languages, NLTK applies techniques like parsing, tokenization, stemming, lemmatisation, and more. Also helps in categorising text, analysing documents, etc.

7. List some libraries of NLTK that are often used in NLP.

NLTK libraries are:

  • Default Tagger
  • Wordnet
  • Treebank
  • Unigram Tagger
  • Regexp Tagger

8. What do you understand by the term Parsing in NLP?

In NLP Parsing is a medium of understanding by a machine between a sentence and grammatical structure. It allows machines to comprehend the importance of words in a sentence and how nouns, phrases, subjects, and objects are grouped together within it. It also enables to analyse of text or documents to extract useful insights from it.

9. What are the methods used for obtaining data in NLP projects?

There are many methods for obtaining data in NLP projects. Below are some of them:

  • Data augmentation: From an existing dataset, an additional dataset can be created through this method.
  • Scraping data from the web: Python or other languages can be used to scrape data from websites that are typically not available in a structured format.
  • By using public datasets: Various datasets are available on websites like Kaggel and Google datasets that can be used for NLP purposes.

10. Name two applications of NLP used today.

NLP interview questions

They are:

  • Chatbots: Used for customer service interactions, designed to resolve basic queries of customers. Provides cost-saving and efficiency for companies. 
  • Online translation: Google translate, powered by NLP converts both written and spoken language to another language. It also assists in pronouncing words/texts correctly. 

11. What is “term frequency-inverse document frequency (TF-IDF)”?

TF-IDF is an indicator of how important a given word is in a document, which helps identify keywords and assists with the process of extraction for categorization purposes.  

TF identifies how frequently a given word or phrase is used. While IDF measures its importance within the document.

12. What is an NLP pipeline, and what does it consist of?

NLP issues can be solved by navigating the following steps ( called a pipeline):

  • Collecting text, whether it’s from web scraping for use of available datasets
  • Cleaning text: through stemming & lemmatization
  • Representation of the text (bag of words method)
  • Training the model
  • Evaluating the model
  • Adjusting & deploying the model

13. What is “ name entity recognition (NER)”?

This is a process that separates components of a sentence to summarize it into its main components. 

NER assists machines in understanding the context by identifying data related to “ who, what, when, and where.”

14. What is Part of Speech (POS) tagging?

Part Of Speech tagging is the process of categorising specific words in a text/document as per their part of speech, based on its context. POS is also called Grammatical tagging. 

15. What is “ latent semantic indexing”?

It is used to extract useful information from unstructured data by identifying different words and phrases that have the same or similar meanings within a given context.

It’s a mathematical method for determining context & obtaining a deeper understanding of the language, widely used by search engines. 

16. What is ‘Stemming’ in NLP?

The process of retrieving root words from a given term is called Stemming. With efficient and effective principles, every token can be broken down to obtain its stem or root word. It is a rule-based system that is renowned for its simple utilization.

17. Name a few methods for tagging parts of speech.

Check below for tagging techniques: 

  • Rule-based tagging
  • Transformation based tagging
  • HMM tagging
  • Memory-based tagging

18. What do you understand by the Bigram model in NLP?

It means leveraging the conditional probability of the preceding word to predict the likelihood of a word in a sequence. It is also important to ascertain all previous words in order to calculate the conditional probability of the preceding word. 

19. List some examples of the n-gram model used in the real world.

They are:

  • Tagging parts of speech
  • Communication enhancement
  • Similarity of words
  • Text input 
  • Generation of natural language

20. What is N-gram in NLP? Briefly explain.

N-grams are a set of words that occur together within a given frame and when computing the n-grams, it usually moves one step ahead. Natural language processing and text mining are required in this process.

21. What do you understand by Markov’s assumptions on the Bigram model?

The Markov assumption postulates that the probability of a word in a phrase is determined exclusively by the preceding word in the sentence, rather than all previous words.

22. What is the Masked Language Model?

This is a type of model that takes a phrase as input and attempts to complete it by predicting the hidden (masked) words accurately.

23. What is word embedding in NLP? 

“Word Embedding” is a way of representing text data in a dense vector while making sure similar words are presented together. For instance: man-woman, frogs – toads.

24. What is Semantic Analysis?

Semantic analysis assists a machine in comprehending the meaning of a text. It utilises various algorithms for interpreting words within sentences. Also, it helps in understanding the structure of a sentence.

Techniques used are:

  • Named entity recognition
  • Word sense disambiguation

25. Name some popularly used word embedding techniques.

WordVec, Glove, FastText and Elmo.

26. What is tokenisation in NLP?

Tokenisation is a process used in NLP to split paragraphs into sentences and sentences into tokens or words. 

27. Difference between NLP and NLU. Explain two each point briefly. 

Natural Language Processing (NLP)

  • A system that manages conversations between computers and people simultaneously.
  • Can parse text based on structure, topography, and grammar. 

Natural Language Understanding (NLU)

  • Assist in solving Artificial Intelligence problems.
  • Helps the machine to analyze the meaning behind the language content.

28. Words represented as vectors are known as Neural Word Embeddings in NLP. True/False.


29. Define Corpus.

A corpus is a compilation of original text or audio that has been organised into datasets.

30. List some initial steps before applying the NLP machine learning algorithm on a corpus.

They are:

  • Eliminating punctuation & white spaces
  • Remove stop words
  • Conversion of lowercase to uppercase

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Q1. What is LDA in NLP?

Ans: LDA or Latent Dirichlet Allocation is a topic modeling algorithm hugely used in natural language processing. LDA is a probabilistic model that produces a collection of topics, each with its own distribution of words, for an assigned set of documents. 

Q.2 In the NLP model, What are the metrics used in testing?

Ans: Accuracy, Precision, Recall, and F1 Score.

Q.3. Name Vectorization Techniques.

Ans: Some popular vectorization techniques are:
a) Count Vectorizer (n-Gram models & Bag of words)
b) Term Frequency-Inverse Document Frequency (TF-IDF vectorizer)
c) word2Vec

Q4. What are Data cleaning methods? 

Ans: They are:
a) Removing stop words
b) Removing punctuations
c) Removing regular expressions

Q.5. What helps in preparing for an NLP interview?

Ans: To prepare for an NLP interview, it is best to get the basics clear. Get blogs that help you understand important topics and clarify basic concepts. Armed with a solid foundation of knowledge, you will be able to navigate your interview with confidence.

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