Top 10 NLP Interview Questions and Answers in 2023 | Full Form of NLP


The full form of NLP is Natural language processing. NLP is a sub-field of linguistic processing, artificial intelligence, and machine learning that mainly focuses on interpreting commands from the user’s end. In other words, it handles the communication between computers and human language. It involves the development of algorithms and models that enable the computer to understand human language by making it valuable and meaningful for it. It helps the computer process and analyze a large amount of data presented in natural language. NLP recasts a large variety of text or speech into valuable data for the computer. 

In the last few decades, we have witnessed great advancement in the field of AI, leading to greater development in language models for natural language processing in artificial intelligence. With the raging proliferation in this domain, there has been a rise in the number of job-seekers for the same as well. Many IT institutes have also added AI to their curriculum with the view of updating their students on recent technological developments. IT institutes have changed their courses from just Data Science to Data Science & Analytics with AI courses.

We understand that cracking industry-based interviews can be a little tougher, but with our top-asked NPL questions and answers, we have got you covered. 

Let’s dive into these natural language processing questions and answers without any further ado.

Top 10 Interview Questions and Answers on NLP:

1. What Is Natural Language Processing, and Why Do We Need It?

NLP is a widely used machine translation application that assists with translating language instructions. We live in a data-driven world, and hence the need to access and process such data is becoming increasingly necessary. And hence, it has become essential to convert data from one language to another through machine translations. NLP methods are in great demand in this form in order to understand the significance of the language and increase its effectiveness. NLP is extensively important, as for humans, language is the primary mode of interaction. 

2. Can You Give Two Real-Life Applications Examples of Natural Language Processing?

A few real-life examples of natural language processing are as follows:

a. ChatGPT-3: ChatGPT-3 is one of the best examples of NLP applications. ChatGPT-3 performs translation, answering questions, and many other tasks based on its transformer-based NLP model. It is mainly helpful to write articles, answer questions and generate codes based on its ability to manage the statistical dependency of different words. It is one of the greatest pre-trained NLP models.

b. Google Translate: Google Translate is one of the other famous examples of NLP models. The main feature of Google Translate is that it assists in translating words or lines from one language to another. It also helps with the pronunciation and meaning of words. It has magnificently changed the course of translation for us in the last few decades. 

Explain the Main Components of NLP.

There are three main components of NLP:

a. Syntactic Analysis: Syntactic Analysis is the process of analyzing sentences with respect to extracting meaning from them. It helps the machine to understand the order of words arranged in a sentence and further uses grammar rules of a language for better combination and arrangement of words in a sentence.

b. Semantic Analysis: Semantic Analysis also known as semantic understanding, is the process of extracting meaning and understanding the meaning of the text. It helps with comprehending the sense of the word, used in different sentences. It aids with turning constructed data into human language.

c. Pragmatic Analysis: Pragmatic Analysis refers to the process of deciphering knowledge that is under the same tone but out of the given data. The main aspect of the pragmatic analysis is to explore the different rays of the context of the given document or text. It also enables the extraction of real-world data with the aim of sufficiently understanding the given data. 

3. What Are Stop Words?

Stop words in the context of Natural Language Processing are considered to be insignificant words. Words such as a, as, an, how, why, the, is occur frequently in any given text, but they do not hold much importance with respect to language decoding from the machine end. Stop words are often eliminated by the system in any given text with the aim of improving its efficiency. Search engines are designed in such a way that they overlook the use of stop words for relevant search results. 

4. What Is Regular Grammar in NLP?

Regular grammar with respect to natural language processing refers to a formal grammar that works on a specific set of rules and restrictions. A set of production rules that outline the replacement and rearrangement of symbols are used to define regular grammars. Each production rule is defined by using a four-tuple notation. The four tuples are as follows:
a. Non-terminal symbol (A)
b. Term Symbol (a)
c. Start Symbol (S)
d. Production rule (A → α)

The four-tuple notation of a regular grammar which are essential components for defining the grammar and generating the language. 

5. What Are Bag-Of-Words?

Bag of words is regarded as a disordered bunch of words and focuses rather on the occurrence of a word in the given word. As machine systems are usually designed to encrypt structured data and hence it treats a document as a messy collection of words, therefore further bifurcating as “bag of words”. It further disregards the order or the grammatical sense of the given document. In simpler words it represents text as a collection of text without its order or grammar. 

6. What Is Word Embedding in NLP?

A method for representing words and documents is word embedding. Word Vector or Word Embedding is a lower-dimensional numeric vector input that represents a word. It enables comparable representations for words with similar meanings. They can also roughly convey meaning. 50 values in a word vector can indicate 50 different attributes.

7. What Is Parts-Of-Speech Tagging?

The process of tagging various tags to words such as noun, adjectives, verbs and more is called parts-of-speech tagging. The software at prior reads through the document and then differentiate the words by tagging. It helps in understanding the roles and functions of each word.
Example: “The cat sat on the mat.”

Parts of Speech Tagging:

  • “The” is tagged as a definite article.
  • “cat” is tagged as a noun.
  • “sat” is tagged as a verb.
  • “on” is tagged as a preposition.
  • “the” (second occurrence) is tagged as a definite article.
  • “mat” is tagged as a noun.

8. Explain What Latent Semantic Indexing Is

Latent Semantic Analysis (LSI) is a technique used in natural language processing and information retrieval to uncover hidden relationships and the meaning of a collection of words and documents. It applies mathematical methods to improve the accuracy of the information during the retrieval process. Singular value decomposition is the method employed for information comprehension. This representation, known as a “latent semantic space,” allows for more effective indexing and helps in finding the hidden meaning and relationships between words and documents by analyzing their co-occurrence patterns. 

9. Explain the Concept of TF-LDF:

Term Frequency or Term Frequency- Inverse Document Frequency is a numerical statistic used to assess the significance of a word in a document within a collection of documents. In simpler words, the (TF) takes into account the number of time a word occurs in a document and also (IDF) reversely  inspects the rarity of the word across the document.

10. The Formula for Calculating TF-LDF:

TF(W) = (Frequency of W in a document)/(The total number of terms in the document)
IDF(W) = log_e(The total number of documents/The number of documents having the term W)


These are the few natural language processing interview questions and answers you can go through and be prepared for. There are also different ways to stand out during your interview; having a prior, detailed course done in this domain can help you shine the most. Many IT institutions train their students in such domains, providing them with the best data science courses. Wishing you all the best for your interview! Thank you so much for sticking to the end.

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