NLP Interview Questions and Answers

Natural language processing (NLP) is an automated way of comprehending or analyzing the intricacies and overall meaning of natural language by extracting vital information from typed or spoken text using machine learning algorithms.

When applying for NLP jobs, many applicants are unaware of the types of questions an interviewer might ask. It is vital to prepare specifically for the interviews and know the basics of NLP. MindMajix has compiled a list of the top 30 NLP interview questions and answers to assist you during the interview process.

We have categorized NLP Interview Questions into 3 levels they are:

Basic

Intermediate

Advanced

Frequently Asked NLP Interview Questions

1. What is Pragmatic Analysis, exactly? 
2. What is POS tagging?
3. In NLP, what are stop words?
4. What exactly is NES?
5. What is the definition of information extraction?
6. Give two instances of real-world NLP uses.
7. List a few ways for tagging parts of speech.
8. Explain the N-gram model in NLP in a few words.
9. What exactly do you mean by word embedding?
10. List a few popular word embedding techniques.

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Basic NLP Interview Questions and Answers

1. What are the natural language processing (NLP) project's lifecycle stages?

The stages of a natural language processing (NLP) project's lifespan are as follows:

• Data collection: Refers to the process of gathering, measuring, and assessing accurate insights for study following defined recognized techniques.
• Data Cleaning: The process of removing or repairing inaccurate, corrupted, incorrectly formatted, duplicate, or incomplete data from a dataset is called data cleaning.
• Data Pre-Processing: Data preparation is transforming raw data into a usable format. Feature Engineering extracts the characteristics, traits, and attributes from raw data.
• Data modeling: Studying data items and their connections with other objects. It is used to investigate data requirements for a variety of commercial operations.
• Model Evaluation: A crucial phase in building a model is model evaluation. It aids in selecting the optimal model to describe our data and predict how well that model will function in the future.
• Model Deployment: Process of making an ML model available for real-world application.
• Monitoring and Updating: Evaluate and analyze production model performance to achieve acceptable quality defined by the use case. It sends out notifications when there are problems with performance and aids in detecting and treating the root cause.

2. What are some examples of typical NLP tasks?

NLP is used to do various tasks, including

• Machine Translation: This assists in translating a text from one language to another.
• Text Summarization: This is used to generate a concise summary of the complete text in the document based on a significant corpus.
• Language modeling: This determines how the following phrase will seem based on the history of preceding words. The auto-complete sentences tool in Gmail is an excellent illustration of this.
• Topic modeling: A technique for determining the subject organization of many documents. It identifies the actual topic of a piece of writing.
• Query Answering: This aids in automatically preparing replies based on a corpus of text and an asked question.
• Conversational Agent: These are voice assistants like Alexa, Siri, Google Assistant, Cortana, and others that we see all the time.
• Information Retrieval: This aids in retrieving relevant documents in response to a user's search query.
• Information Extraction: The extraction of useful information from a text, such as an email calendar event, is known as information extraction.
• Text Classification: This is used to categorize a given text into a set of categories based on its content. It is also utilized in many AI-based applications, including sentiment analysis and spam identification.

3. How do Conversational Agents work?

Conversational Agents use the following NLP components:

• Voice Recognition and Synthesis: Speech recognition aids in converting speech impulses to phonemes, which are subsequently transcribed as words.
• Natural Language Understanding (NLU): The transcribed text from stage one is analyzed using AI algorithms within the natural language understanding system. Named Entity Recognition, Text Classification, Language Modeling, and other NLP tasks are relevant here.
• Management of Conversations: After extracting the necessary data from text, we proceed to the stage of determining the user's purpose. The user's response can then be categorized as a pre-defined intent using a text classification system. This assists the conversational agent in determining what is being asked.
• Generating Answer: The agent creates an appropriate response based on the initial phases' semantic understanding of the user's purpose.

4. What does data augmentation imply? What are some examples of data augmentation techniques used in NLP projects?

NLP offers various ways for taking a small dataset and combining it with other data to build larger datasets. Data augmentation is the term for this. Language attributes are used to generate text that is syntactically comparable to the original text data.

The following are some examples of how data augmentation may be used in NLP projects:

• Entities are being replaced.
• Word substitution based on the TF-IDF
• Back data translation with noise
• Synonym replacement
• Bigram flipping.

5. What procedures should you take while creating a text categorization system?

The following stages are commonly performed while establishing a text categorization system:

1. Gather or create a labeled dataset suitable for the task.
2. After separating the dataset into two (training and test) or three sections (training, validation (i.e., development), and test sets), choose an assessment measure (s).
3. Unprocessed text is converted into feature vectors.
4. To train a classifier, use the feature vectors and labels from the training set.
5. Using the evaluation metric(s) from Step 2, compare the model's performance on the test set.
6. To service a real-world use case, deploy the model and track its performance to service a real-world use case.

6. Describe how parsing is done in NLP?

The process of recognizing and comprehending a text's syntactic structure is parsing. It's done by dissecting the text's fundamental parts. Each word is parsed one at a time, then two at a time, three at a time. When the machine parses the text one word at a time, it's called a unigram. A bigram is a text in which two words are analyzed at a time. A trigram is a group of three words that the computer parses simultaneously.

The following points will help us understand why parsing is vital in NLP:

• The parser will report any syntax mistakes.
• It assists in the recovery of frequently recurring mistakes, allowing the rest of the program to be processed.
• The parse tree is created using a parser.
• The parser creates a symbol table, vital in natural language processing.
• In addition, to construct intermediate representations, a Parser is used (IR).

7. What exactly is a "Bag of Words" (BOW)?

The Bag of Words model, which employs word frequency or occurrences to train a classifier, is a popular one. This approach creates a matrix of occurrences for texts or phrases, regardless of grammatical structure or word order.

A bag-of-words is a text representation that indicates how often words appear in a document. It consists of two steps:

A glossary of well-known terms—a metric for determining if they exist.

The document is a "bag" of words since all information about the order or organization of words is removed. The model is just concerned with whether or not recognized terms occur in the document, not with the place of those phrases.

 8. What is Regular Grammar?

Regular grammar represents a common language.

A -> a, A -> aB, and many additional rules exist in regular grammar. The rules allow for the identification and analysis of strings to be automated.

In regular grammar, there are four tuples:

• ‘N’ represents the non-terminal set.
• ‘∑’ represents the set of terminals.
• ‘P’ stands for the set of productions.
• ' € N’ denotes the start of non-terminal.

9. What is Latent Semantic Indexing (LSI) in Natural recovering using?

The mathematical approach of Latent Semantic Indexing (LSI), also known as Latent Semantic Analysis, is used to improve the accuracy of information retrieval. It facilitates the finding of hidden (latent) associations between words (semantics) by developing a collection of various ideas connected with a phrase's terms to improve information understanding. The NLP approach used for this is singular value decomposition. It works well with small sets of static content.

10. What are some of the measures used to assess NLP models?

The following are some of the measures used to evaluate NLP models:

• Accuracy is utilized when the output variable is categorical or discrete. It is the model's proportion of right predictions relative to the total number of forecasts.
• The precision parameter indicates how accurate or exact the model's predictions are, i.e., how many positive (the class we're interested in) instances can the model properly identify.
• F1 score: This measure combines precision and recalls into a single metric that indicates the accuracy-recall trade-off, i.e., completeness and exactness.
• The formula for F1 is (2 Precision Recall) / (Precision + Recall).
• The AUC measures the number of correct positive predictions vs the number of wrong positive predictions as the prediction threshold is modified.

NLP Intermediate Interview Questions

11. What is Pragmatic Analysis, exactly?

In NLP, pragmatic analysis is a crucial job for understanding knowledge that exists outside of a given document. Using pragmatic analysis aims to concentrate on a specific component of a document or text in a language. This necessitates a thorough understanding of the real world. The pragmatic analysis helps software programs to know the true meaning of phrases and words through critical interpretation of real-world data.

12. How can data for NLP projects be obtained?

There are several methods for obtaining data for NLP projects. The following are a few:

Using publicly accessible datasets: Datasets for NLP may be found on sites such as Kaggle and Google Datasets.

Using data augmentation: This technique produces new datasets from current ones.

Scraping data from the web: Using Python or other programming languages, one may scrape data from websites that aren't generally available in an organized format.

13. What do Text Extraction and Cleanup imply?

Text extraction and cleaning is the process of extracting raw text from input data while removing all non-textual information such as markup, metadata, and other non-textual information and converting the text to the needed encoding type. This is usually determined by the format of the available data for the project.

The following are some of the most frequent methods for text extraction in NLP.

• Sentiment Analysis for Named Entity Recognition
• Summarization of Text
• Topic Modeling using Aspect Mining

14. What actions are required in resolving an NLP issue?

The steps for addressing an NLP problem are as follows:

• Obtain the text via scraping the web or using the provided dataset.
• For text cleaning, use stemming and lemmatization.
• Feature engineering strategies should be used.
• Use word2vec to embed
• Neural networks or other Machine Learning techniques can train the created model.
• Examine the model's results.
• Make the necessary adjustments to the model.
• Set up the model.

15. What are Regular Expressions?

To match and tag words, a regular expression is employed. It is made up of a set of characters that are used to match strings.

If A and B are regular expressions, then they must satisfy the following conditions:

• It is a regular language, then for it is a regular expression.
• A + B is a regular expression within the language A, B if A and B are regular expressions.
• The concatenation of A and B (A.B) is a regular expression if A and B are regular expressions.
• A* (A occurring multiple times) is a regular expression if A is a regular expression.

16. What is the difference between Natural Language Processing (NLP) and Natural Language Understanding (NLU)?

Natural Language Processing (NLP)

• NLP is a system that handles simultaneous end-to-end talks between computers and people.
• In NLP, both humans and robots are engaged.
• NLP is concerned with understanding language in its purest form, as stated.
• Grammar, structure, typography, and point of view may all be used to parse text using NLP.

Natural Language Understanding(NLU)

• NLU assists in resolving Artificial Intelligence's most complex challenges.
• NLU transforms unstructured inputs into structured text, allowing machines to comprehend them
• NLU, on the other hand, focuses on obtaining context and meaning or determining what was intended.
• NLU will assist the machine in deducing the meaning of the linguistic material.

17. What is a Masked Language Model, and how does it work?

By generating an output from the defective input, masked language models assist learners in comprehending deep representations in downstream tasks. This approach is frequently used to anticipate the words in a phrase.

18. What is POS tagging?

POS tagging, or parts of speech tagging, is the basis for identifying individual words in a document and classifying them as part of speech based on their context. Because it entails analyzing grammatical structures and selecting the appropriate component, POS tagging is also known as grammatical tagging.

Because the same word might be several parts of speech depending on the context, POS tagging is a complicated procedure. Because of the same reason, the same general approach used for word mapping is unsuccessful for POS tagging.

19. What exactly is NES?

The practice of recognizing certain entities in a text document that are more informative and have a distinct context is known as named entity recognition (NER). These are frequently referred to as places, individuals, organizations, and others. Even though these things appear to be proper nouns, the NER approach does not recognize them. In reality, NER entails entity chunking or extraction, which includes segmenting entities into many specified classes. This stage also aids in the extraction of data.

20. What exactly is NLTK? What distinguishes it from Spacy?

Natural Language Toolkit (NLTK) is a set of libraries and applications for processing symbolic and statistical natural language. This toolkit includes some of the most sophisticated libraries for breaking down and understanding human language using machine learning approaches. Lemmatization, Punctuation, Character Count, Tokenization, and Stemming are all done with NLTK. The following are the differences between NLTK and Spacey:

• While NLTK provides various programs to pick from, Spacey's toolkit only contains the best-suited algorithm for a given scenario.
• In comparison to Spacey, NLTK supports many languages (Spacey supports only seven languages)
• NLTK provides a string processing library, but Spacey has an object-oriented library. Spacey can handle word vectors, whereas NLTK cannot.

21. What is the definition of information extraction?

In the context of Natural Language Processing, information extraction refers to the process of mechanically extracting structured information from unstructured sources to assign meaning to it. This might involve retrieving entity properties, relationships between entities, and more. The following are some examples of information extraction models:

• Module for Taggers
• Module for Extracting Relationships
• Module for Fact Extraction
• Module for Extracting Entities
• Module for Sentiment Analysis
• Module for Network Graphs
• Module for Document Classification and Language Modeling.

22. What are the most effective NLP tools?

Some of the most excellent open-source NLP tools are

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

23. List 10 use cases to be solved using NLP techniques?

• Sentiment Analysis
• Language Translation (English to German, Chinese to English, etc..)
• Document Summarization
• Question Answering
• Sentence Completion
• Attribute extraction (Key information extraction from the documents)
• Chatbot interactions
• Topic classification
• Intent extraction
• Grammar or Sentence correction
• Image captioning
• Document Ranking
• Natural Language Inference.

24. In NLP, what are stop words?

Stop words are common words that appear in sentences and provide weight to the phrase. These stop words serve as a link between phrases, ensuring grammatically accurate. Stop words are taken out before natural language data is processed, and they are a frequent pre-processing strategy.

Advanced NLP Interview Questions

25. In NLP, what is stemming?

Stemming is the process of extracting the root word from a given term. With efficient and well-generalized principles, all tokens may be broken down to retrieve the root word or stem. It's a rule-based system that's well-known for its ease of use.

26. Give two instances of real-world NLP uses?

1. Spelling/Grammatical Checking Apps: NLP algorithms are used in mobile applications and websites that help users fix grammar problems in the submitted text. These days, they may even suggest the next few words that the user might input, thanks to the employment of particular NLP models on the backend.

2. ChatBots: Many websites now provide customer service via virtual bots that talk with users and help them solve problems. It functions as a filter for concerns that do not require engagement with the customer service representatives of the firms.

27. Define Dependency Parsing?

Dependency parsing is a technique for understanding grammatical structure by highlighting the relationships between its components. It investigates how the words of a phrase are related linguistically. Dependencies are the names given to these connections.

28. What is the difference between false positives and false negatives?

A false negative occurs when a machine learning system incorrectly forecasts a negative outcome as positive.

A false positive is defined as a machine learning system that incorrectly forecasts a positive outcome as a negative.

29. List a few ways for tagging parts of speech?

Rule-based tagging, HMM tagging, transformation-based tagging, and memory-based tagging are all examples of tagging techniques.

30. List a few examples of how the n-gram model is used in the real world?

1. Communication Enhancement

2. Tagging of parts of speech

3. Generation of natural language

4. The similarity of Words

5. Identification of Authorship

6. Sentiment Analysis

7. Text Input Predictive.

31. In NLP, what is the bigram model?

A bigram model is an NLP model that uses the conditional probability of the preceding word to predict the likelihood of a word in a phrase. It is critical to know all of the last words to calculate the conditional probability of the previous word.

32. What are your impressions of the Masked Language Model?

The Masked Language Model is a model that takes a phrase as input and attempts to finish it by accurately predicting a few concealed (masked) words.

33. List the types of sorts available in linguistic ambiguity?

1. Lexical Ambiguity: This sort of ambiguity occurs when a phrase has homonyms and polysemy.

2. Syntactic Ambiguity: Syntactic ambiguity occurs when the grammar of a statement allows for many interpretations.

3. Semantic Ambiguity: When a statement comprises ambiguous words or phrases with unclear meanings, this ambiguity occurs.

34. Explain the N-gram model in NLP in a few words?

The N-gram model is an NLP model that predicts the likelihood of a word in a phrase based on the conditional probability of n-1 preceding terms. The essential idea behind this method is that rather than utilising all of the previous words to predict the future word, we simply utilise a handful of them.

35. What is the bigram model's Markov assumption?

For the bigram model, the Markov assumption assumes that the probability of a word in a phrase depends solely on the preceding word in that sentence rather than all last words.

36. What exactly do you mean by word embedding?

Word embedding is the method of expressing textual data using a real-number vector in natural language processing. This technique allows words with similar meanings to be represented simultaneously.

37. What is an embedding matrix, and how does it work?

A word embedding matrix is a matrix that contains all of the words in a text's embedding vectors.

38. List a few popular word embedding techniques?

A few word embedding approaches are listed below.

• Word2Vec Glove
• Embedding Layer.

39. What are the first few steps you'll take before applying a natural language processing (NLP) machine-learning algorithm on a corpus?

1. Eliminating white spaces
2. Eliminating Punctuation
3. Lowercase to Uppercase Conversion
4. Tokenisation 
5. Getting Rid of Stopwords
6. Lemmatization.

40. What is the difference between an hapax and an hapax legomenon?

Hapaxes are unusual words that only appear once in a sample text or corpus. Each one is referred to as an hapax or hapax legomenon ('read-only once' in Greek). It's also known as a singleton.