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Top 38+ Most Asked NLP Interview Questions and Answers

1) What is the full form of NLP? / What is Natural Language Processing?

NLP stands for "Natural Language Processing". NLP is a field of computer science that deals with communication between computer systems and humans. This technique uses Artificial Intelligence and Machine Learning to create automated software that helps understand the human spoken languages and extract useful information from the data gathered from the audio.

The techniques used in NLP allow computer systems to process and interpret data in the form of natural languages. It designs algorithms that can extract meaning from large datasets in audio or text format by applying machine learning algorithms. In other words, we can say that NLP is software that uses artificial intelligence and machine learning algorithms to understand natural languages or the way human beings read and write in a language and extracts required information from such data.

2) What are some real-life applications / real-world examples of Natural Language Processing (NLP)?

Some real-life applications of NLP or Natural Language Processing are as follows:

Spelling/Grammar Checking Apps: Spelling and grammar checking applications are real-life examples of Natural Language Processing. These apps are mainly used in mobile applications and websites that facilitate users to correct grammar mistakes in the entered text rely on NLP algorithms. They also recommend the best possible substitutes that the users might type. This is possible because of specific NLP models being used in the backend.

Google Translate: Google Translate is the most famous application of Natural Language Processing. Using this, you can convert your written or spoken sentences into any language. You can also get the correct pronunciation and meaning of a word by using Google Translate. The Google Translate application uses some advanced techniques of Natural Language Processing to provide translation of sentences into various languages.

Chatbots apps: Chatbots applications provide a better customer support service. Many websites and companies use this to offer customer support through these virtual bots that chat with the user and resolve their problems. Many companies use chatbots for 24/7 service to resolve the basic queries of customers. Generally, it filters the basic issues that do not require an interaction with the companies' customer executives. It makes the customers feel that the customer support team quickly attends them. If a chatbot cannot resolve any user's query, it forwards it to the support team while still engaging the customer. Chatbots also make companies capable of building cordial relations with customers. These all are only possible because of Natural Language Processing.

3) What are the most used NLP (Natural Language Processing) Terminologies?

Following is the list of most used NLP (Natural Language Processing) Terminologies:

  • Preprocessing: This is a method used to remove unwanted text or noise from the given text and make it "clean." It is the first step of any NLP task. s
  • Documents: Documents are the body of text and are collectively used to form a corpus.
  • Corpus, or Corpora (Plural): It is a collection of text of similar type, for example, movie reviews, social media posts, etc.
  • Vocabulary: It is a group of terms used in a text or speech.
  • Out of Vocabulary: It specifies the terms not included in the vocabulary. We put the terms created during the model's training in this category.
  • Tokenization: It is used in NLP to break down large sets of text into small parts for easy readability and understanding. Here, the small parts are referred to as 'text' and provide a piece of meaningful information.
  • N-grams: It specifies the continuous sequence (similar to the power set in number theory) of n-tokens of a given text.
  • Parts of Speech (POS): It specifies the word's functions, such as a noun, verb, etc.
  • Parts of Speech Tagging: It is the process of tagging words in the sentences into different parts of speech.

4) What are the most used NLP (Natural Language Processing) Terminologies?

Following is the list of most used NLP (Natural Language Processing) Terminologies:

  • Embeddings (Word): This process is used to embed each token as a vector and then pass it into a machine learning model. We can apply embeddings also on phrases and characters, apart from words.
  • Stop Words: These are used to remove the unwanted text from further text processing, for example, a, to, can, etc.
  • Transformers: Transformers are deep learning architectures that can parallelize computations. They are used to learn long-term dependencies.
  • Normalization: This is a process of mapping similar terms to a canonical form, i.e., a single entity.
  • Lemmatization: Lemmatization is a type of normalization used to group similar terms to their base form according to their parts of speech. For example, talking and talking can be mapped to a single term, talk.
  • Stemming: Stemming is also a type of normalization similar to lemmatization. But, it is different in the term that it segregates the words without the parts of speech tags. It is faster than lemmatization and also be more precise in some cases.

5) What are some of the major components of Natural Language Processing?

Following is a list of some of the major components of Natural Language Processing:

Entity extraction: It is used for segmenting a sentence to identify and extract entities, such as a person (real or fictional), organization, geographies, events, etc. 85

Pragmatic analysis: Pragmatic analysis extracts information from the input text. It is part of the process of data extraction.

Syntactic analysis: Syntactic analysis is used for the proper ordering of words.

6) What do you understand by Dependency Parsing in NLP or Natural Language Processing?

In Natural Language Processing, Dependency Parsing is a process of assigning syntactic structure to a sentence and identifying its dependency parses. This is an important process to understand the correlations between the "head" words in the syntactic structure. That's why it is also known as syntactic parsing.

The process of dependency parsing becomes a little complex if there are more sentences that have more than one dependency parses. Multiple parse trees are known as ambiguities. The main task of dependency parsing is to resolve these ambiguities to assign a syntactic structure to a sentence effectively. It is also used in semantic analysis apart from syntactic structuring.

7) What are some most common areas of usage of Natural Language Processing?

Following is a list of some most common areas of usage of Natural Language Processing:

  • Semantic Analysis
  • Text classification
  • Automatic summarization
  • Questioning Answering

Some real-life examples of Natural Language Processing are chatbots, IOS Siri, Google Assistant, Amazon echo, Spelling, grammar checking apps, and Google translate.

8) What do you understand by NLTK in Natural Language Processing?

In Natural Language Processing, NLTK stands for Natural Language Toolkit. It is a Python library used to process data in human spoken languages. NLTK facilitates developers to apply parsing, tokenization, lemmatization, stemming techniques, and more to understand natural languages. It is also used for categorizing text, parsing linguistic structure, analyzing documents, etc.

Following is the list of some libraries of the NLTK package that are often used in NLP:

  • DefaultTagger
  • UnigramTagger
  • RegexpTagger
  • backoff_tagger
  • SequentialBackoffTagger
  • UnigramTagger
  • BigramTagger
  • TrigramTagger
  • treebank
  • wordnet
  • FreqDist
  • Patterns etc.

9) What is the use of TF-IDF? Why is it used in Natural language Processing?

In Natural language Processing, tf-idf, TF-IDF, or TFIDF stands for Term Frequency-Inverse Document Frequency. It is a numerical statistic used to specify how important a word is to a document in a collection or the collection of a set.

10) What is the difference between formal and natural languages?

The main difference between a formal language and a natural language is that a formal language is a collection of strings. Each string contains symbols from a finite set called alphabets. On the other hand, a natural language is a language that humans use to speak. This is completely different from a formal language as it contains fragments of words and pause words like uh, um, etc.

11) What are the tools used for training NLP models?

The most common tools used for training NLP models are NLTK, spaCY, PyTorch-NLP, openNLP, etc.

12) What do you understand by information extraction? What are the various models of information extraction?

In Natural Language Processing, information extraction is a technique of automatically extracting structured information from unstructured sources to get useful information. It extracts information such as attributes of entities, the relationship between different entities, and more.

Following is a list of various models of information extraction in Natural Language Processing:

  • Fact Extraction Module
  • Entity Extraction Module
  • Sentiment Analysis Module
  • Tagger Module
  • Relation Extraction Module
  • Network Graph Module
  • Document Classification and Language Modeling Module

13) What are the stop words in Natural Language Processing?

In Natural Language Processing, stop words are regarded as useless data for a search engine. It includes the words like articles, prepositions, was, were, is, am, the, a, an, how, why, and many more. The algorithm used in Natural Language Processing eliminates the stop words to understand and analyze the meaning of the sentences. Eliminating the stop words is one of the most important tasks for search engines to process data.

Software developers design the algorithms of search engines so that they ignore the use of stop words and only show the relevant search result for a query.

14) What is Bag of Words in Natural Language Processing?

Bag of Words is a commonly used model in Natural Language Processing that depends on word frequencies or occurrences to train a classifier. This model creates an occurrence matrix for documents or sentences without depending on their grammatical structure or word order.

15) What do you understand by semantic analysis? What are the techniques used for semantic analysis?

Semantic analysis is a process that makes a machine understand the meaning of a text. It uses several algorithms to interpret the words in sentences. It is also used to understand the structure of a sentence.

Following are the techniques used for semantic analysis:

Named entity recognition: This technique is used to specify the process of information retrieval that helps identify the entities like the name of a person, organization, place, time, emotion, etc.

Natural language generation: This technique specifies a process used by the software to convert the structured data into human spoken languages. By using natural language generation, organizations can automate content for custom reports.

Word sense disambiguation: It technique is used to identify the sense of a word used in different sentences.

16) What is pragmatic ambiguity in NLP?

Pragmatic ambiguity is used to specify words with more than one meaning, and they can be used in any sentence depending on the context. In pragmatic ambiguity, words have multiple interpretations.

Pragmatic ambiguity occurs when the meaning of the words is not specific. For example, if a word gives different meanings. Due to pragmatic ambiguity, a sentence can have multiple interpretations. Sometimes, we come across sentences that have words with multiple meanings, making the sentence open to interpretation.

17) What is Latent Semantic Indexing (LSI)? What is the use of this technique?

LSI or Latent Semantic Indexing is a mathematical technique used in Natural Language Processing. This technique is used to improve the accuracy of the information retrieval process. The LSI algorithm is designed to allow machines to detect the latent correlation between semantics.

The machines generate various concepts to enhance information understanding. The technique used for information understanding is called singular value decomposition. It is mainly used to handle static and unstructured data. This is one of the best-suited models to identify components and group them according to their types.

Latent Semantic Indexing or LSI is based on a principle that specifies that words carry a similar meaning when used in a similar context. The computational LSI models are slow compared to other models, but they can improve a text or document's analysis and understanding.

18) What do you understand by MLM in Natural Language Processing?

In Natural Language Processing, MLM is a term that stands for Masked Language Model. It helps learners understand deep representations in downstream tasks by taking the output from the corrupt input.

This model is mainly used to predict the words used in a sentence.

19) What are the most commonly used models to reduce data dimensionality in NLP?

The most commonly used models to reduce the dimensionality of data in NLP are TF-IDF, Word2vec/Glove, LSI, Topic Modelling, Elmo Embeddings, etc.

20) What is Lemmatization in Natural Language Processing?

Lemmatization is a process of doing things properly using a vocabulary and morphological analysis of words. It is mainly used to remove the inflectional endings only and return the base or dictionary form of a word, known as the lemma. It is just like cutting down your beard or shaving to get the original shape of your face.

For example: girl's = girl, bikes= bike, leaders= leader etc.

So, the main task of Lemmatization is to identify and return the root or original words of the sentence to explore various additional information.

21) What is Stemming in Natural Language Processing?

Stemming is a process of extracting the base form of a word by removing the affixes from them. It is just like cutting down the branches of a tree to its stems.

For example: After stemming, the words go, goes, and going would be 'go'.

Search engines use stemming for indexing the words. It facilitates them to store only the stems rather than storing all forms of a word. By using stemming, the search engines reduce the size of the index and increase the retrieval accuracy.

22) What is the difference between Stemming and Lemmatization in NLP?

Stemming and Lemmatization are both the text normalization techniques used in Natural language Processing. Both are used to prepare text, words, and documents for further processing. They seem very similar techniques, but there are quite differences between them. Let's see the main differences between them:

Stemming Lemmatization
Stemming is the process of extracting the base form of a word by removing the affixes from them. It produces the morphological variants of a root/base word. Stemming programs are commonly known as stemming algorithms or stemmers. Lemmatization is a more advanced process and looks beyond word reduction, just like stemming. It considers a full vocabulary of a language and applies a morphological analysis to words.
For example, the lemma of 'went' is 'go', and the lemma of 'mice' is 'mouse'.
Stemming is not as much informative as Lemmatization. It is a somewhat crude method for cataloging related words. It essentially cuts letters from the end until the stem is reached. Lemmatization is much more informative than simple Stemming; that is why Spacy has opted to only have Lemmatization available instead of Stemming.
Stemming is not as efficient as Lemmatization. This method works fairly well in most cases, but unfortunately, English has many exceptions requiring a more sophisticated process. Lemmatization is more efficient than Stemming as it works well in exceptional words.
Following are some examples of Stemming:
run: run
runner: runner
running: run
ran: ran
runs: run
easily: easili
fairly: fair etc.
Following are some examples of Lemmatization:
run: run
runner: run
running: run
ran: run
runs: run
goes: go
go: go
went: go
saw: see
mice: mouse

23) Which NLP techniques use a lexical knowledge base to obtain the correct base form of the words?

The NLP techniques that use a lexical knowledge base to obtain the correct base form of the words are Lemmatization and stemming.

24) What is tokenization in Natural Language Processing?

In Natural Language Processing, tokenization is a method of dividing the text into various tokens. These tokens are the form of the words, just like a word forms into a sentence. In NLP, the program computers process large amounts of natural language data. These large amounts of natural language data have to be cut into shorter forms. So, tokenization is an important step in NLP that cuts the text into minimal units for further processing.

25) What are some open-source libraries used in NLP?

Some popular open-source libraries used in NLP are NLTK (Natural Language ToolKit), SciKit Learn, Textblob, CoreNLP, spaCY, Gensim, etc.

26) What are the key differences between NLP and NLU?

Following is the list of key differences between NLP and NLU:

NLP is a short form of Natural Language Processing. NLU is a short form of Natural Language Understanding.
NLP or Language Processing is used to create a system that can make and establish communication between humans and computers. NLU or Natural Language Understanding provides techniques that can solve complicated problems related to machine understanding.
It includes all the techniques required for the interaction between computers and humans. It converts the uncategorized input data into a structured format and allows the computers to understand the data.
It includes the techniques focused on analyzing "what is said?" It includes the techniques to understand "what is meant?"

27) What are the key differences between NLP (Natural Language Processing) and CI (Conversational Interface)?

Following is the list of key differences between NLP (Natural Language Processing) and CI (Conversational Interface):

Natural Language Processing (NLP) Conversational Interface (CI)
The full form of NLP is Natural Language Processing. The full form of CI is Conversational Interface.
The main focus of NLP is to make computers understand and learn how the normal human being languages' concepts work. The main and only focus of CI is to provide users with an interface to interact with.
Natural Language Processing uses AI technology to identify, understand, and interpret users' requests through languages. CI or Conversational Interface uses voice, chat, videos, images, and other conversational aid to create the user interface for communication.

28) What do you understand by Pragmatic Analysis?

Pragmatic analysis is an important task used in Natural Language Processing for interpreting knowledge lying outside a given document. It is mainly implemented to focus on exploring a different aspect of the document or text in a language. It requires a comprehensive knowledge of the real world to make software applications capable of critical interpretation of the real-world data to know the actual meaning of sentences and words.

For example, see the following sentence:

'Do you know what time it is?'

This sentence can be used to ask for knowing the time or for yelling at someone to make them note the time. It completely depends on the context in which this sentence is used.

29) What are the best open sources of NLP Tools available in the market?

Some of the best open sources NLP tools available in the market are:

  • SpaCy
  • TextBlob
  • Textacy
  • Natural language Toolkit (NLTK)
  • Retext
  • NLP.js
  • Stanford NLP
  • CogcompNLP etc.

30) How can you differentiate Artificial Intelligence, Machine Learning, and Natural Language Processing?

Following are the key differences between Artificial Intelligence, Machine Learning, and, Natural Language Processing:

Artificial Intelligence Machine Learning Natural Language Processing
Artificial Intelligence is a technique used to create smarter machines and computers. Machine Learning is a term used for systems that learn from experience. Natural Language Processing or NLP is the set of systems that can understand the languages used by humans and process these languages to make them understood by computers.
Artificial Intelligence requires human intervention. Without human intervention, it is not possible to create intelligent machines. Machine Learning doesn't require human intervention. It purely involves the working of computers and machines. Natural Language Processing uses both computer and human languages to work properly.
Artificial Intelligence is a broader concept than Machine Learning. It includes a lot of working fields. Machine Learning is a narrow concept and is a subset of Artificial Intelligence. Natural Language Processing uses the concept of both Artificial Intelligence and Machine Learning to make the tools that can process human language and make it understandable by machines.

31) What do you understand by POS tagging?

The full form of POS tagging is Parts of speech tagging. It is most commonly known as POS tagging. According to their context, it specifies a process of identifying specific words in a document and groups them as part of speech.

POS tagging is also known as grammatical tagging because it involves understanding grammatical structures and identifying the respective component. It is a very complicated process because the same word can be different parts of speech depending on the situation and the structure of the sentence.

32) What is NES in Natural Language Processing? Why is it used?

NES is an acronym that stands for Name Entity Recognition. It is used in Natural Language Processing and is most commonly known as NER. It is the process of identifying specific entities in a text document that is more informative and have a unique context. It includes places, people, organizations, and more. After identification, it extracts these entities and categorizes them under different predefined classes. Later, this step helps in extracting information.

33) What is parsing in Natural Language Processing? What are the different types of parsing used in NLP?

Parsing is a technique or a method of analyzing the sentences automatically according to their syntactic structure.

Following is a list of different types of parsing used in Natural Language Processing:

Dependency parsing / Syntactic parsing: Dependency parsing is also known as syntactic parsing. It recognizes a dependency parse of a sentence and assigns a syntax structure to the sentence. It mainly focuses on the relationship between different words.

Semantic parsing: Semantic parsing is a method of converting the natural language into machine language that a computer can understand and process.

Constituency parsing: Constituency parsing is a specific parsing method where a division of sentences is divided into sub-parts or constituencies. It is mainly used to extract a constituency-based parse tree from the constituencies of the sentences.

Shallow parsing / Light parsing: Shallow parsing is also known as light parsing and chunking. It identifies constituents of sentences and then links them to different groups of grammatical meanings.

34) What is language modeling in NLP?

In Natural Language Processing, language modeling creates a probability distribution of a sequence of words. It provides probability to all the words present in that sequence.

35) What is topic modeling in NLP?

In NLP, topic modeling is finding abstract topics in a document or set of documents to find hidden semantic structures.

36) What is the key difference between dependency parsing and shallow parsing?

The key difference between dependency parsing and shallow parsing is that dependency parsing is the process of finding a relation between all the different words. On the other hand, shallow parsing is the parsing of a selected limited part of the information.

37) What do you understand by Pragmatic Ambiguity in NLP?

In Natural Language Processing, pragmatic ambiguity specifies multiple descriptions of a word or a sentence. It occurs when the words of the sentence may have different meanings, and the correct meaning of the sentence is not clear. In this case, it becomes very difficult for a machine to understand a sentence's meaning, which causes pragmatic ambiguity.

For example, see the following sentence:

"Are you feeling hungry?"

The above sentence could be either a generally asked question or a formal way of offering food.

38) What are the steps used to solve an NLP problem?

Following is a list of steps used to solve an NLP problem:

  • In the first step, get the text from the available dataset.
  • Now, apply stemming and lemmatization to clean the text.
  • Now, apply feature engineering techniques to the received text.
  • Embed using word2vec.
  • Now, train the built model using neural networks or other Machine Learning techniques.
  • Now it turns to evaluate the model's performance.
  • Make the appropriate changes in the model.
  • Now, the model is complete. Deploy the model.

39) What is noise removal in NLP? Why is it used?

Noise removal is one of the NLP techniques. As the name specifies, it is used to remove unnecessary pieces of text from the sentences.

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