Top 25+ NLP Interview Questions and Answers

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Natural Language Processing (NLP) is a field of AI that helps computers understand human language. It began in the 1950s with pioneers like Alan Turing and later gained momentum with the work of Noam Chomsky. Today, NLP is used in chatbots, voice assistants, sentiment analysis, and more. Jobs like NLP engineer, machine learning scientist, and AI researcher are in high demand. If you are preparing for one of these roles, knowing the right NLP interview questions can give you a real edge. Let’s look at some of the most commonly asked questions and their answers.

Fun Fact – According to the National Institutes of Health (NIH), NLP can identify specific and meaningful concepts with up to 98% accuracy.

NLP Interview Questions for Freshers 

Here are some common natural language processing interview questions that help beginners and prepare for entry-level roles.

1. What is NLP?

Natural Language Processing (NLP) is a field of AI that helps machines read, understand, and respond to human language. It combines linguistics with machine learning to process text or speech. Common uses include –

• Chatbots
• Translation tools
• Virtual assistants like Siri or Alexa

2. What are the stages in the lifecycle of an NLP project?

An NLP project usually follows these steps –

• Data Collection: Gather relevant raw data.
• Data Cleaning: Fix or remove errors and inconsistencies.
• Preprocessing: Normalize text through tokenization, stemming, etc.
• Feature Engineering: Extract useful features for model input.
• Modeling: Train ML or deep learning models.
• Evaluation: Check model accuracy, precision, recall, etc.
• Deployment: Launch the model in a real-world system.
• Monitoring: Track performance and make updates if needed.

3. What is tokenization and why is it important in NLP?

Tokenization is the process of splitting text into smaller units, called tokens. These could be words, characters, or subwords. It is important because NLP models work with tokens, not raw text. Tokenization helps structure the data for better analysis and model performance.

4. How do you handle out-of-vocabulary (OOV) words in NLP models?

To manage OOV words, we often use subword tokenization like BPE or WordPiece. These break unknown words into smaller parts the model understands. Some models also use character-level embeddings or fallback to similar known word vectors.

5. What is stemming and how does it differ from lemmatization?

Stemming chops off word endings using rules, which can create non-words. Lemmatization uses a dictionary and grammar rules to return the correct base form. For example, “running” becomes “run” via lemmatization but “runn” via stemming.

6. What is Named Entity Recognition (NER), and why is it used?

Named Entity Recognition (NER) is an NLP technique that identifies and categorizes entities like names, places, and dates in text. It is used to extract structured information from unstructured data for tasks like search, analysis, and automation.

NLP Interview Questions for Experienced

These interview questions on NLP are commonly asked in senior-level roles to assess your practical expertise and deep understanding of language models and applications. 

7. Explain TF-IDF and its use in text analysis.

TF-IDF stands for Term Frequency–Inverse Document Frequency. It helps us find important words in a document by comparing how often they appear across different texts. Common words like “the” get low scores. Unique words get higher scores. It’s widely used for keyword extraction, document similarity, and search relevance.

8. What is dependency parsing and why does it matter?

Dependency parsing identifies grammatical relationships between words in a sentence. For example, it can detect that “dog” is the subject of “barked.” This structure helps machines understand sentence meaning. It’s especially useful in question answering and chatbot development, where clarity of meaning matters.

9. Define perplexity and how it evaluates language models.

Perplexity measures how well a language model predicts a sample. Lower perplexity means the model is more confident and accurate. It’s calculated using the inverse probability of the test sequence. In simple terms, it tells us how “surprised” the model is by the text. Less surprise is better.

10. What is pragmatic ambiguity and how do you handle it?

Pragmatic ambiguity happens when meaning depends on context or real-world knowledge. For instance, “Can you pass the salt?” is a request, not a question about ability. To handle it, I try combining world knowledge, conversation history, and intent classification. Sometimes even small cues can change the meaning.

11. Explain latent semantic indexing (LSI) and its use case.

LSI is a technique that finds patterns in word usage across documents. It uses matrix decomposition to group words and texts by meaning, even if they don’t share exact terms. I have used LSI for document clustering and topic detection in customer feedback analysis.

12. How do you evaluate an NLP model – mention key metrics.

For classification, I check accuracy, precision, recall, and F1 score. For language tasks like translation or summarization, I use BLEU or ROUGE scores. I also test performance on real-world data to see how well it generalizes.

NLP Coding Interview Questions

This section covers NLP interview questions that focus on coding skills using Python and popular NLP libraries.

13. Write code to tokenize a sentence and remove stop words using Python.

import nltk

from nltk.corpus import stopwords

from nltk.tokenize import word_tokenize

nltk.download(‘punkt’)

nltk.download(‘stopwords’)

text = “Natural Language Processing is a fascinating field.”

tokens = word_tokenize(text)

filtered_tokens = [word for word in tokens if word.lower() not in stopwords.words(‘english’)]

print(filtered_tokens)

14. Implement a function to build TF-IDF vectors from a document corpus.

from sklearn.feature_extraction.text import TfidfVectorizer

corpus = [

    “NLP is used in chatbots and search engines.”,

    “TF-IDF stands for term frequency–inverse document frequency.”,

    “It helps identify important words in documents.”

]

vectorizer = TfidfVectorizer()

tfidf_matrix = vectorizer.fit_transform(corpus)

print(vectorizer.get_feature_names_out())

print(tfidf_matrix.toarray())

15. How would you implement BPE or WordPiece tokenization from scratch?

from collections import Counter

def get_pairs(word):

    pairs = set()

    prev_char = word[0]

    for char in word[1:]:

        pairs.add((prev_char, char))

        prev_char = char

    return pairs

def bpe_tokenize(corpus, num_merges=10):

    vocab = {” “.join(word): count for word, count in Counter(corpus).items()}

    for _ in range(num_merges):

        pairs = Counter()

        for word, freq in vocab.items():

            symbols = word.split()

            for pair in get_pairs(symbols):

                pairs[pair] += freq

        if not pairs:

            break

        best_pair = pairs.most_common(1)[0][0]

        new_vocab = {}

        for word in vocab:

            new_word = word.replace(” “.join(best_pair), “”.join(best_pair))

            new_vocab[new_word] = vocab[word]

        vocab = new_vocab

    return vocab

corpus = [“low”, “lower”, “newest”, “widest”]

tokenized = bpe_tokenize(corpus)

print(tokenized)

16. Write code to perform named entity recognition using a library like SpaCy or NLTK.

import spacy

nlp = spacy.load(“en_core_web_sm”)

doc = nlp(“Barack Obama was the 44th President of the United States.”)

for ent in doc.ents:

    print(ent.text, ent.label_)

Also Read - Top 15+ Python Automation Interview Questions and Answers

NLP Engineer Interview Questions

Here are some frequently asked interview questions and answers for NLP engineering roles.

17. Compare BERT vs GPT: what are the architectural differences?

BERT is a bidirectional encoder, meaning it reads text from both left and right to understand full context. It uses masked language modeling during training. GPT is a unidirectional decoder that predicts the next word from left to right. GPT is better for generation tasks, while BERT is better for understanding tasks.

18. Describe the transformer model architecture and how it handles long-range dependencies.

The transformer uses self-attention to assign weights to words based on their relevance to each other. It processes all words in parallel, unlike RNNs that go one by one. This allows it to capture relationships between distant words in a sentence, which helps with tasks like summarization and translation.

19. How do you fine-tune a pre-trained transformer on a downstream task?

I start by selecting a pre-trained model like BERT or RoBERTa. Then I add a task-specific head – like a classification layer. I freeze or unfreeze layers depending on dataset size and task complexity. Finally, I train it on labeled data with a lower learning rate to avoid overfitting.

20. Discuss techniques to reduce bias in NLP models.

I review the training data for bias first. Then I apply techniques like data balancing, counterfactual data generation, and adversarial training. After training, I test outputs for bias using fairness metrics. If needed, I retrain using corrected data or adjust predictions post-hoc.

NLP Viva Questions

Here are some quick NLP questions often asked during viva exams to test your understanding of key concepts and terminology.

21. What is the difference between NLP and NLU?

NLP stands for Natural Language Processing. It deals with how machines process and understand human language in general. NLU, or Natural Language Understanding, is a subfield of NLP that focuses on extracting meaning and context. NLP is broader, while NLU is more about comprehension.

22. Define an n-gram and give an example of its use.

An n-gram is a sequence of ‘n’ words from a text. For example, in the phrase “I love NLP,” the bigrams are “I love” and “love NLP.” N-grams are used in language modeling, autocomplete, and speech recognition.

23. What is POS tagging and why is it important?

POS tagging assigns each word in a sentence its part of speech, like noun or verb. It helps the model understand grammar and context. For example, in “She runs fast,” “runs” is a verb, not a noun.

24. What is a bag-of-words model?

It represents text by counting how often each word appears, ignoring grammar and order. While simple, it’s effective for basic classification tasks like spam detection or topic categorization.

25. Explain what semantic similarity means in NLP.

It measures how close two texts are in meaning. For instance, “I’m happy” and “I feel great” are semantically similar. I have used cosine similarity on sentence embeddings to measure this in sentiment matching projects.

Other Important NLP Interview Questions

Now let’s go through some additional NLP interview questions that can further strengthen your preparation.

Transformers NLP Interview Questions

1. Explain self-attention and multi-head attention. 
2. How does positional encoding work in transformers? 
3. What is masked language modelling? 
4. Differences between transformer encoder and decoder. 
5. Explain layer normalization and residual connections. 

Data Science NLP Interview Questions

1. What steps are involved in text pre-processing for ML pipelines? 
2. How do you extract features from text data? 
3. Compare generative vs discriminative models in NLP. 
4. What evaluation metrics do you use for classification vs translation tasks? 
5. How do you handle imbalanced classes in text classification? 

Also Read - Top 50+ Data Science Interview Questions and Answers

Deep Learning NLP Interview Questions

1. Compare LSTM vs GRU networks and when you would use each. 
2. What are CRFs and how are they used in sequence tagging? 
3. Explain attention mechanisms in neural networks. 
4. What are transformers and why do they outperform RNNs? 
5. How do you fine-tune a BERT model?

Machine Learning NLP Interview Questions

1. How would you use Naive Bayes in text classification? 
2. Explain the Markov assumption in n-gram models. 
3. What is TF-IDF and why is it effective? 
4. How do you evaluate a model using confusion matrix metrics? 
5. When would you prefer rule-based over ML-based NLP?

Also Read - Top 90+ Machine Learning Interview Questions and Answers

Google NLP Interview Questions

1. How would you optimize a transformer model for latency on Google-scale data?
2. Describe how to build a multilingual NER system at scale.
3. Explain using zero-shot learning to support a new language without retraining. 
4. How do you implement semantic search using vector embeddings?
5. How do you detect and remove bias in Google’s language models?

Amazon NLP Interview Questions

1. How would you implement intent detection in Alexa’s conversational system?
2. Describe methods to scale sentiment analysis across millions of customer reviews.
3. How do you manage streaming text input in customer service chatbots?
4. Explain deploying a transformer for real-time inference with AWS Lambda.
5. How do you test and evaluate entity extraction at large scale?

Tips to Prepare for NLP Interview

Here are some practical tips to help you prepare for NLP interviews –

• Review key concepts like tokenization, embeddings, and transformers
• Practice coding tasks in Python using real datasets
• Build a small project like a chatbot or sentiment analyzer
• Understand evaluation metrics like F1 score and BLEU
• Go through common NLP questions for interview to identify patterns and weak areas
• Stay updated with new models like LLaMA and GPT-4

Wrapping Up

These 25+ NLP interview questions cover everything from basics to advanced concepts asked in real interviews. Practice them well, understand the logic behind each answer, and keep experimenting with projects. 

Looking for NLP job openings? Find top IT roles on Hirist, where freshers and experienced professionals find the right opportunities.

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