40 Interesting NLP Interview Questions and Answers [Includes All 3 Levels]

Are you preparing for an NLP interview but unsure where to start or what kind of questions you might face? Natural Language Processing (NLP) is a fast-evolving domain at the intersection of linguistics and machine learning, and it’s increasingly in demand across industries, from chatbots and virtual assistants to sentiment analysis and document processing. 

To help you navigate interviews confidently, we’ve compiled the top 40 NLP interview questions and answers, categorized by beginner, intermediate, and advanced levels. 

Whether you’re a fresher, a transitioning developer, or a seasoned ML enthusiast, this article is designed to sharpen your concepts and get you interview-ready. Without further ado, let us get started!

Quick Answer

NLP interview questions often cover text preprocessing, tokenization, stemming vs lemmatization, word embeddings, language models, named entity recognition, sentiment analysis, and sequence modeling. Knowing how these work, when to use them, and common challenges will help you answer questions confidently.

Beginner-Level NLP Interview Questions and Answers

If you’re just getting started with Natural Language Processing, these questions will help reinforce your understanding of the core concepts. Expect questions on foundational topics like tokenization, stop words, Bag of Words, and basic libraries. 

1. What is NLP?

Natural Language Processing (NLP) is a field within Artificial Intelligence (AI) that focuses on enabling machines to interact with, understand, and generate human language. This involves both syntactic (structure) and semantic (meaning) processing.

2. What is the difference between NLP and NLU?

While the terms are often used interchangeably, they refer to different layers of understanding in language-based systems:

• NLP (Natural Language Processing) refers to the broader field that includes reading, understanding, generating, and translating human language using machines.
  
• NLU (Natural Language Understanding) is a subdomain of NLP that focuses on extracting meaning from text, essentially understanding the user’s intent.

3. Explain Tokenization in NLP.

Tokenization is the first and most fundamental step in NLP preprocessing. It involves splitting a large chunk of text into smaller pieces called tokens. These tokens could be:

• Words: “NLP is cool.” → [“NLP”, “is”, “cool”, “.”]
• Sentences: Break an entire paragraph into individual sentences
• Subwords: Common in modern language models like BERT

Tokenization helps machines understand text in a structured form and serves as the foundation for further processing like parsing, tagging, and vectorization.

4. What is stemming and lemmatization?

Both stemming and lemmatization are techniques to reduce words to their root form, but they differ in method and accuracy.

• Stemming: Applies heuristics to chop off suffixes. It might produce non-existent words.
  
  • “running” → “run”
  • “happily” → “happili”
    
• Lemmatization: Uses vocabulary and morphological analysis to get the correct root word (lemma).
  
  • “better” → “good”
  • “running” → “run”

5. What are Stop Words?

Stop words are common words in a language that are usually filtered out in NLP preprocessing. These words, like “the”, “is”, and”, “in”, don’t carry significant meaning and can add noise to models.

6. What is the Bag of Words model?

The Bag of Words (BoW) model is a simple and widely used technique to represent text data in numerical form.

Here’s how it works:

• Each document is converted into a “bag” of its words, ignoring grammar and word order.
• The model keeps count of how often each word occurs.

Despite its simplicity, BoW doesn’t capture meaning or context, which leads to limitations in complex NLP tasks.

7. What is TF-IDF?

TF-IDF stands for Term Frequency–Inverse Document Frequency. It’s an improved version of the Bag of Words model that scores words based on their importance to a document relative to a corpus.

• Term Frequency (TF): How often a word appears in a document
• Inverse Document Frequency (IDF): Measures how unique a word is across all documents

TF-IDF helps in reducing the weight of common words and increasing the weight of rare but meaningful words.

8. Name a few popular NLP libraries in Python.

Here are some widely used Python libraries for NLP:

• NLTK (Natural Language Toolkit): Great for educational purposes, supports tokenization, POS tagging, parsing, etc.
• spaCy: Industrial-strength NLP library with blazing speed and efficiency.
• TextBlob: Simple to use, especially for beginners.

Each library has its strengths depending on the use case and scale of your project.

9. What is Named Entity Recognition (NER)?

NER is the process of locating and classifying named entities in text into predefined categories such as:

• Person names
• Organizations
• Locations
• Dates
• Percentages

10. What is the difference between Rule-Based and Statistical NLP?

• Rule-Based NLP: Relies on predefined linguistic rules and grammar created by experts. Effective in controlled domains but not scalable for large or ambiguous datasets.
• Statistical NLP: Uses machine learning algorithms trained on large corpora to learn language patterns. It can generalize better, but requires a lot of data.

In real-world applications, many modern NLP systems combine both approaches to leverage precision and adaptability.

11. What is a corpus in NLP?

A corpus is a large and structured collection of text used for training or evaluating NLP models. For example, news articles, books, or tweets can form a corpus. It provides real-world text data for analysis and model building.

12. What is text preprocessing, and why is it important?

Text preprocessing involves cleaning and preparing raw text for analysis, including removing punctuation, converting to lowercase, and tokenization. It is important because it standardizes text and improves model accuracy.

13. What is collocation in NLP?

Collocations are combinations of words that frequently appear together, such as “strong tea” or “make a decision.” Identifying collocations helps in understanding natural language patterns and building better language models.

14. What is part-of-speech (POS) tagging used for?

POS tagging labels words with their grammatical category, such as noun, verb, or adjective. It helps in understanding sentence structure, improving parsing, and enabling tasks like information extraction.

15. What is text vectorization?

Text vectorization is converting words or text into numerical representations so that machine learning models can process them. Techniques include Bag of Words, TF-IDF, or word embeddings.

Intermediate-Level NLP Interview Questions and Answers

Once you’ve nailed the basics, interviewers will start testing your ability to work with word vectors, similarity metrics, classification models, and context understanding. 

The questions in this section focus on practical implementation and model-driven thinking, essential for mid-level roles and hands-on projects.

16. What are word embeddings?

Word embeddings are dense vector representations of words in a continuous vector space, where semantically similar words are mapped closer together.

Unlike Bag of Words or TF-IDF, which produce sparse and high-dimensional vectors, embeddings like Word2Vec, GloVe, and FastText encode semantic relationships. For instance:

• Vector(“king”) – Vector(“man”) + Vector(“woman”) ≈ Vector(“queen”)

17. Explain Word2Vec and its two architectures.

Word2Vec is a popular word embedding model introduced by Google. It learns word relationships based on surrounding context using two architectures:

• CBOW (Continuous Bag of Words): Predicts the target word from surrounding context words.
• Skip-Gram: Predicts surrounding context words given a target word.

Both are trained using shallow neural networks and produce meaningful word vectors.

18. What is cosine similarity, and how is it used in NLP?

Cosine similarity measures the cosine of the angle between two vectors. In NLP, it’s widely used to:

• Compare document similarity
• Match user queries with search results
• Measure semantic similarity between word embeddings

Formula:

cos(θ) = (A · B) / (||A|| * ||B||)

19. What is the difference between precision, recall, and F1-score in NLP classification?

These are evaluation metrics for classification tasks like sentiment analysis or NER.

• Precision: Of all predicted positives, how many are correct?
  • Formula: TP / (TP + FP)
    
• Recall: Of all actual positives, how many did we catch?
  • Formula: TP / (TP + FN)
    
• F1-Score: Harmonic mean of precision and recall. A balanced metric.
  • Formula: 2 * (Precision * Recall) / (Precision + Recall)

20. How does POS tagging work?

POS (Part-of-Speech) tagging assigns grammatical categories—such as noun, verb, adjective—to each word in a sentence.

For example:

• “The quick brown fox jumps over the lazy dog.”
  
  • “The” → Determiner
  • “fox” → Noun
  • “jumps” → Verb

Techniques used:

• Rule-based tagging (using grammar rules)
• Statistical models (like Hidden Markov Models)
• Neural network-based models (like BiLSTMs)

POS tagging is crucial for syntactic parsing, question answering, and coreference resolution.

21. What is dependency parsing?

Dependency parsing identifies grammatical relationships between words in a sentence. It determines which words depend on others and how.

For example:

• Sentence: “She eats an apple.”
• Dependency Tree:
  
  • “eats” → Root
  • “She” → Subject of “eats”
  • “apple” → Object of “eats”

Libraries like spaCy can perform dependency parsing with built-in models.

22. What is a language model?

A language model (LM) is trained to predict the next word in a sequence based on the previous words. It’s fundamental to NLP tasks like text generation, autocomplete, and translation.

Types:

• N-gram models: Predict based on the previous N-1 words (e.g., bigram, trigram)
• Neural LMs: Use RNNs, LSTMs
• Transformer-based models: Like GPT and BERT, capable of understanding long-range dependencies

23. What is the difference between BERT and GPT?

In simple terms:

• BERT reads both left and right context → better understanding.
• GPT predicts the next word using left context only → better generation.

24. How do you handle out-of-vocabulary (OOV) words?

OOV words are those not seen during training. Handling them is important to avoid loss of meaning or breakdown of the model.

Strategies:

• UNK token: Replace unknown words with a universal token.
• Subword tokenization: Break words into known pieces (e.g., BERT uses WordPiece).
• Character-level models: Encode words using characters, not vocabulary.
• Use pre-trained embeddings: More likely to contain rare words.

Modern NLP models like BERT and GPT handle OOV words better through subword techniques.

25. Give a code snippet for text classification using scikit-learn.

Here’s a simple example of building a text classification model using TfidfVectorizer and MultinomialNB.

from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.naive_bayes import MultinomialNB

from sklearn.pipeline import make_pipeline

# Sample training data

train_texts = ["I love NLP", "This is amazing", "Terrible experience"]

train_labels = ["positive", "positive", "negative"]

# Create model pipeline

model = make_pipeline(TfidfVectorizer(), MultinomialNB())

# Train model

model.fit(train_texts, train_labels)

# Predict

test_texts = ["NLP is fantastic"]

print(model.predict(test_texts))  # Output: ['positive']

This pipeline is a great starting point for tasks like sentiment analysis or spam detection.

26. What is FastText, and how is it different from Word2Vec?

FastText represents words as a combination of character n-grams, allowing it to generate embeddings for rare or unseen words. Word2Vec generates static embeddings only for words seen during training. For example, if “NLPer” is unseen, Word2Vec cannot produce a vector, but FastText can approximate it using subword information. This improves model performance on languages with many word forms.

27. What is POS chunking in NLP?

POS chunking groups words into meaningful phrases, such as noun phrases (NP) or verb phrases (VP). For example, in “The quick brown fox jumps,” POS tagging identifies “The/DT quick/JJ brown/JJ fox/NN” as a noun phrase. Chunking simplifies sentence analysis, helps extract structured information, and is used in question answering and named entity recognition.

28. What is Latent Semantic Analysis (LSA)?

LSA reduces the dimensionality of text data and identifies hidden relationships between words and documents. For instance, it can detect that “car” and “automobile” are related. LSA uses singular value decomposition (SVD) on the term-document matrix to capture semantic meaning. It is used in topic modeling, information retrieval, and document similarity tasks.

29. What is word sense disambiguation (WSD)?

WSD identifies the correct meaning of a word based on context. For example, “bank” in “river bank” vs. “financial bank.” WSD improves tasks like machine translation, search, and text understanding by resolving ambiguity. Methods include supervised learning with labeled corpora, knowledge-based approaches using dictionaries, and unsupervised clustering techniques.

30. What are contextual embeddings?

Contextual embeddings represent words differently depending on the surrounding text. For example, BERT generates separate vectors for “bank” in “river bank” vs. “bank account.” Unlike static embeddings (Word2Vec, GloVe), contextual embeddings capture word meaning in context, improving accuracy for tasks like sentiment analysis, NER, and question answering.

Advanced-Level NLP Interview Questions and Answers

At the advanced level, you’re expected to understand the inner workings of transformer architectures, attention mechanisms, fine-tuning strategies, and ethical considerations in NLP. 

These questions are common in research-oriented roles, product-based companies, or senior AI/NLP positions where depth of understanding is critical.

31. What are Transformers in NLP?

Transformers are a type of deep learning architecture introduced in the paper “Attention is All You Need” (Vaswani et al., 2017). They revolutionized NLP by enabling models to process entire sequences in parallel rather than sequentially (as in RNNs or LSTMs).

Transformers are the backbone of models like BERT, GPT, T5, RoBERTa, and more.

32. Explain the Attention Mechanism.

Attention is a method that allows models to focus on relevant parts of the input sequence when generating outputs. It helps the model understand which words are more important in context.

Types:

• Bahdanau attention: Additive attention used in RNN-based encoder-decoder models.
• Scaled dot-product attention: Used in transformers.

33. What is self-attention in Transformers?

Self-attention is a specific kind of attention mechanism where each word in a sentence considers every other word (including itself) to build a better representation.

For example, in the sentence:

“The animal didn’t cross the street because it was tired.”

Self-attention helps the model understand that “it” refers to “animal” rather than “street.”

34. How does BERT handle input sequences?

BERT uses a specific input format and tokenization strategy:

• [CLS]: A special classification token added at the beginning.
• [SEP]: Separator token used for sentence-pair tasks (e.g., QA, NLI).
• Token Embeddings: Each word or subword is converted into an embedding.
• Segment Embeddings: Distinguish sentence A and B (in case of pair input).
• Positional Embeddings: Encodes the position of tokens.

BERT is trained using Masked Language Modeling (MLM) and Next Sentence Prediction (NSP) tasks.

35. What are some ethical concerns in NLP?

As NLP models grow more powerful, several ethical issues emerge:

• Bias: Models can reflect gender, racial, or cultural biases present in training data.
• Misinformation: Large language models can generate fake or misleading content.
• Privacy: Models may inadvertently memorize and leak sensitive information.
• Toxicity: Unfiltered models may produce offensive or harmful language.
• Overreliance: Excessive dependence on language models without human oversight can be risky.

36. What is masked language modeling (MLM)?

MLM is a training method where some words in a sentence are masked, and the model predicts them using the surrounding context. For example, “I love [MASK]” should predict “NLP.” MLM is used in BERT to teach the model bidirectional understanding, allowing it to capture context from both left and right of a word.

37. What is transfer learning in NLP?

Transfer learning reuses a pre-trained model for a new, related task. For example, a BERT model trained on Wikipedia can be fine-tuned for sentiment analysis. This reduces training time, requires less data, and improves performance, since the model already understands language patterns.

38. What is token type embedding in Transformers?

Token type embeddings distinguish different segments in the input text. For example, in BERT’s question-answering tasks, segment A could be the question and segment B the passage. These embeddings allow the model to understand relationships between segments, improving tasks like next sentence prediction.

39. What is positional encoding in Transformers?

Since Transformers process all tokens simultaneously, they need positional encoding to capture the order of words. Positional encoding adds vectors representing token positions to input embeddings, allowing the model to understand word sequences and relationships.

40. What are sequence-to-sequence (Seq2Seq) models used for?

Seq2Seq models map input sequences to output sequences. They use encoder-decoder architectures with attention mechanisms. Applications include machine translation (English → French), text summarization, and chatbots. Attention allows the model to focus on relevant parts of input when generating each output token.

Bonus: Scenario-Based Networking Questions and Answers

Beyond theoretical knowledge, many interviews test how well you can apply NLP techniques in realistic situations. These scenario-based questions assess your ability to think critically, make design decisions, and troubleshoot language-related problems:

1. You’re building a chatbot for customer support. How would you design it to handle ambiguous user inputs?

To handle ambiguity, I would implement the following strategies:

• Intent classification confidence threshold: If the model’s confidence is low, prompt the user for clarification.
• Fallback responses: Use a default flow to ask questions that narrow down the user’s intent.
• Context tracking: Maintain conversation context to resolve ambiguity based on prior exchanges.
• NER and POS tagging: Identify key entities or action verbs to infer likely intent.

Using pre-trained models like BERT for intent recognition, combined with rule-based decision trees, would offer a robust hybrid approach.

2. Your sentiment analysis model wrongly classifies sarcastic comments as positive. What would you do?

Sarcasm is a known limitation for most sentiment models. To improve this:

• Dataset enhancement: Introduce sarcastic examples into the training data.
• Use contextual models: Fine-tune transformer-based models like RoBERTa or DeBERTa, which can better understand subtle tones.
• Feature engineering: Include punctuation (e.g., excessive “!” or “…”) and keyword patterns (like “Yeah, right”) as features.
• Multimodal cues (if applicable): If it’s a social media post with emojis or images, these can provide additional context for sarcasm.

3. You need to extract company names from job descriptions, but many aren’t well-known entities. How do you approach NER?

Standard NER models may fail to recognize lesser-known or startup company names. My approach would include:

• Custom NER training: Use spaCy or HuggingFace’s Transformers to train a new model with annotated job descriptions.
• Data labeling: Create a labeled dataset with domain-specific company names.
• External knowledge integration: Use a business name database (e.g., LinkedIn or Crunchbase API) to validate or auto-correct predictions.
• Post-processing: Apply pattern-based rules (e.g., terms after “at”, “by”, or “hiring”) to boost recall.

4. You’re building a search feature and need to match user queries with FAQs. What NLP techniques would you use?

To match semantically similar questions and answers:

• Embedding-based similarity: Convert queries and FAQs to embeddings using Sentence-BERT or Universal Sentence Encoder.
• Cosine similarity: Rank FAQs based on cosine similarity scores with the user query.
• TF-IDF fallback: In case embeddings aren’t available, use TF-IDF vectors for keyword-based matching.
• Intent detection: Classify the query intent first, then match it within a narrowed-down FAQ cluster.

For speed and scalability, I’d use vector databases like FAISS for real-time similarity search.

5. You are working on voice transcription and notice frequent errors in entity names (e.g., “Dell” becomes “tell”). How do you fix this?

In speech-to-text systems, named entity recognition is often challenged by homophones and uncommon terms. To handle this:

• Custom vocabulary: Update the speech recognition model’s vocabulary to include brand names and entities.
• Post-processing NER: Apply a context-aware NER model on transcribed text to detect and correct entities.
• Phonetic matching: Use phonetic similarity algorithms like Soundex or Metaphone to match misheard entities with likely candidates.

Combining ASR correction layers with post-transcription NER usually yields the best results.

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Conclusion

In conclusion, mastering NLP isn’t just about understanding concepts—it’s about being able to apply them, explain them clearly, and solve real-world problems with them. 

These 30 carefully selected questions will help you assess your knowledge, identify gaps, and approach your NLP interviews with confidence. As NLP continues to shape the future of AI-driven applications, staying sharp with both fundamentals and cutting-edge techniques will give you a significant advantage. 

Keep practicing, stay curious, and you’ll be well on your way to cracking your next interview.