Prashant Sharma — Published On August 29, 2022
Beginner Interview Questions Interviews NLP

This article was published as a part of the Data Science Blogathon.


Natural language processing (NLP) is the branch of computer science and, more specifically, the domain of artificial intelligence (AI) that focuses on providing computers the ability to understand written and spoken language in a way similar to that of humans.

Combining computational linguistics (rule-based modeling of human language) with statistical, machine learning, and deep learning models is natural language processing (NLP). Together, these technologies enable computers to ‘understand’ the whole meaning of human language in the form of text or speech data, including the speaker’s or writer’s purpose and emotion.

NLP is the driving force behind computer systems that translate text from one language to another, respond to spoken commands, and swiftly summarise massive amounts of information—even in real-time. There is a strong probability that you have engaged with NLP through voice-activated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. NLP plays an increasing role too in corporate solutions that optimize business operations, boost staff productivity, and simplify mission-critical business procedures.

What is the importance of natural language processing?

Businesses use the massive volume of unstructured, text-heavy data and require a method for processing it efficiently. Most of the data produced online and saved in databases are natural human languages. Until recently, organizations were unable to inspect this data efficiently. Herein lies the utility of natural language processing.

NLP Interview Questions

1. What do you mean by NLTK?

NLTK, which stands for Natural Language Toolkit, is a Python library. We use NLTK to process spoken language data. NLTK facilitates the application of techniques like parsing, tokenization, lemmatization, and stemming from comprehending natural languages. It aids in text categorization, parsing linguistic structure, document analysis, etc.

2. What is Parsing in regards to NLP?

In natural language processing, parsing refers to a machine’s understanding of a sentence’s grammatical structure. Parsing enables a device to understand the meaning of a word in a sentence along with the grouping of words, phrases, nouns, subjects, and objects. Parsing facilitates the analysis of a text or document to uncover valuable information.

3. What do you mean by Syntactic Analysis?

Syntactic analysis is a method used to derive meaning from sentences. A machine can examine and comprehend the order of words in a phrase through syntactic analysis. NLP makes use of the grammar rules of a language to aid in the syntactic analysis of the combination and order of words in texts.

Sentence: the dog saw a man in the park


NLP Interview Questions

4. In NLP, what is Pragmatic Ambiguity?

Pragmatic ambiguity refers to words with several meanings whose usage in any given sentence is context-dependent. The same language may have several meanings due to pragmatic ambiguity. Most phrases we encounter contain words with several meanings, leaving them open to interpretation. This varied interpretation results in ambiguity and is referred to in NLP as Pragmatic Ambiguity.

5. What does Stemming imply in NLP?

Stemming is the process of eliminating suffixes from words to get their root form. It is comparable to chopping a tree’s branches into its trunk. For instance, the stem of eating, eats, and eaten is eat. Search engines index the words using stemming. Stemming is crucial for natural language comprehension (NLU) and natural language processing (NLP).

NLP Interview Questions

6. What do you mean by POS tagging?

Parts of speech tagging, often known as POS tagging, is the process of detecting individual words in a document and classifying them based on their context as parts of speech. POS tagging is also referred to as grammatical tagging since it requires understanding grammatical structures and identifying the corresponding component.POS tagging is a complex approach since the same word can have several meanings depending on context. For the same reason, the same basic approach employed for word mapping is unsuccessful for POS tagging.

7. What does Lemmatization imply in NLP?

Lemmatization is mapping a word’s different forms to its root (also known as the “lemma”). Although this may look similar to the definition of stemming, it is distinct. For instance, after stemming, the word “better” remains unchanged. Upon lemmatization, however, this should become “excellent.” Lemmatization requires a deeper understanding of language. Modeling and designing effective lemmatizers is still an open question in NLP research.

8. What does Text Normalization mean in NLP?

Consider a scenario in which we are using social media posts to discover event details. The terminology used in social media posts may be vastly different from that of, say, newspapers. A phrase may be spelled in several ways, including in abbreviated versions (with and without hyphens), names are often written in lowercase, etc. When designing NLP tools to work with such data, it is advantageous to get a canonical representation of text that incorporates these differences into a single representation. This process is known as text normalization. Common text normalization steps include converting all text to lowercase or uppercase, converting numbers to text (e.g., 7 to seven), and extending abbreviations.

9. What does TF-IDF mean in Natural language processing?

TF-IDF, also known as Term Frequency-Inverse Document Frequency, is a method for determining the significance of a word relative to other terms in a corpus. It is a typical metric for information retrieval (IR) and summarization scoring. TF-IDF translates words to vectors and adds semantic information, resulting in weighted uncommon words that may be utilized in several NLP applications.

10. What is NES?

Named entity recognition, or NER, is finding entities in a written document that are more informative and have their context. These frequently signify locations, individuals, and organizations. Even though these items appear to be proper nouns, the NER method identifies much more than simply the nouns. In actuality, NER entails entity chunking or extraction, in which entities are split to classify them under several predetermined classifications. This process helps extract information further.


One of the most significant advantages of NLP is that it enables computers to communicate with humans using natural language. Additionally, other language-related behaviors are measured. Thanks to Natural Language Processing, computers can now hear, analyze, measure, and identify which portions of speech are relevant (NLP). NLP applications include chatbots, sentiment analysis, and market intelligence. Since its introduction, NLP’s popularity has increased. Today, technologies such as Amazon’s Alexa are widely used around the globe. Moreover, business intelligence and consumer monitoring are rapidly gaining pace and will soon dominate the sector for enterprises. Key takeaways from the article:

  • NLP is an area of computer science and AI (AI)
  • In NLP, parsing is a machine’s knowledge of a sentence’s grammar.
  • When you remove a word’s suffix, you return it to its original form.
  • TF-IDF stands for Term Frequency-Inverse Document Frequency

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About the Author

Prashant Sharma

Currently, I Am pursuing my Bachelors of Technology( B.Tech) from Vellore Institute of Technology. I am very enthusiastic about programming and its real applications including software development, machine learning and data science.

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