35+ Essential NLP Interview Questions and Answers to Excel in 2025

Did you know that India's job market for AI and ML roles is increasing by 36%, including NLP roles in 2025? As the demand for NLP technologies rises in e-commerce, healthcare, etc., NLP interview questions are essential for people looking to make a career in AI. 

When preparing for an NLP interview, the most common topics typically focus on text preprocessing, feature extraction, and model evaluation. These NLP Interview Questions often test your understanding of language models, algorithm selection, and practical applications.

Interviewers will evaluate your expertise in text classification, named entity recognition (NER), and semantic analysis. Additionally, you’ll need to demonstrate your familiarity with tools like SpaCy, TensorFlow, and Hugging Face Transformers. Preparing for technical rounds, aptitude tests, and HR interviews will ensure you're ready to showcase your problem-solving skills. 

This article highlights key interview questions on NLP​ to help you confidently prepare for your interview.

Looking to develop your skills for NLP interview questions? upGrad’s Artificial Intelligence & Machine Learning - AI ML Courses can help you learn the tools and strategies to enhance your expertise on interview questions on NLP​. Enroll now!

NLP Interview Questions for Freshers: Key Preparation Tips

For freshers preparing for NLP interview questions, foundational concepts like tokenization, stop word removal, and text processing techniques form the basics of NLP. You must understand statistical methods like TF-IDF to understand word importance within a corpus and capture semantic relationships. 

In addition, demonstrating practical experience with NLP libraries like NLTK, SpaCy, or Hugging Face’s transformers can help demonstrate practical NLP experience.

If you want to learn essential skills to learn NLP and AI that can help you build your career, the following courses from upGrad can help you succeed. 

• Generative AI Mastery Certificate for Managerial Excellence
• Executive Diploma in Machine Learning and AI with IIIT-B
• Master’s Degree in Artificial Intelligence and Data Science

Here are some of the interview questions on NLP​ for beginners. 

1. What is Natural Language Processing (NLP), and why is it important?

Natural language processing (NLP) is a subfield of AI that focuses on enabling computers to understand, interpret, and generate human languages. It involves using ML algorithms, statistical models, and deep learning techniques to process and analyze natural language data. NLP enables machines to interpret human language semantics, enabling sentiment analysis and translation tasks.

• You can use TensorFlow, PyTorch, and Hugging Face, popular deep learning frameworks for NLP tasks.
• NLTK and SpaCy are libraries in Python that support text processing and NLP tasks.
• Some significant applications include speech recognition, such as Siri and Google Assistant, sentiment analysis, and language translation, such as Google Translate. 

NLP enables machines to understand context, meaning, and intent in human language, essential for automating tasks in customer service, healthcare, education, and more.

2. What are stop words, and how are they handled in NLP?

Stop words are common words like is, in, and on, that occur frequently in text but provide little value in the analysis of meaning. In NLP, stop words are often removed during text preprocessing to reduce computational complexity and improve the performance of models by focusing on significant terms.

• Tokenization: The process of splitting text into individual words or phrases. You can remove stop words after tokenization to prevent irrelevant data from affecting model performance.
• Modeling Impact: For tasks like text classification, removing stop words helps you focus on content-rich terms, improving accuracy and reducing the dimensionality of feature vectors.
• Libraries: Python's NLTK and SpaCy offer built-in lists of stop words that can be removed automatically.
• Techniques: You can customize stop words for specific NLP tasks, depending on the context, for example, domain-specific terms.

3. What are the main stages in an NLP pipeline?

An NLP pipeline involves steps that transform raw text into valid structured data that can be processed using ML models. The stages include text processing, representation, feature extraction, and model training. 

Here’s a comprehensive overview of the stages of the NLP pipeline. 

• Step 1: Text processing: The first step is tokenization, where you split texts into individual words or sentences. Moreover, lowercasing and removing stop words and stemming reduce words from their root forms, for example, running to run. 
• Step 2: Text representation: Bag of words (BoW) represents texts as a collection of word frequencies. You can use TF-IDF (Term Frequency-Inverse Document Frequency) to measure the importance of a word relative to a corpus. Moreover, you can convert words into dense vectors at this stage, for example, with Word2Vec and GloVe. 
• Step 3: Feature extraction:  In this stage, you identify entities such as names, organizations, and dates using named entity recognition (NER). In addition, you can use part-of-speech (POS) tagging to identify grammatical components, such as nouns, verbs, and more. 
• Step 4: Model training: In this step, you implement SVM, naive bayes, or neural networks using TensorFlow or Scikit-learn. 
• Step 5: Output Interpretation & Action Mapping: This is the final step where you refine the outputs from your models and convert predictions into actionable results. 

4. What is parsing in the context of NLP?

Parsing refers to analyzing the grammatical structure of a sentence to identify relationships between words. It breaks down a sentence into its constituents or grammatical components, critical for understanding the syntactic structure.

Types of parsing: 

• Constituency Parsing: Breaks the sentence into sub-phrases, nouns, and verb phrases.
• Dependency Parsing: Focuses on relationships between words, showing how one word depends on another.

Libraries:

• SpaCy: Provides built-in parsers for both dependency and constituency parsing.
• NLTK: It offers parsing capabilities for different models.

Techniques:

• You can use shift-reduce parsing, a bottom-up parsing approach used to build syntactic trees. 
• Another technique is top-down parsing, which starts from the root and breaks down the sentence into vital components. 

Parsing is necessary for downstream tasks such as machine translation and question answering, where the structure of the sentence directly influences meaning. 

5. What is Named Entity Recognition (NER)?

Named Entity Recognition (NER) is a critical NLP task that involves identifying and classifying entities in text, such as the names of people, organizations, and more. NER uses sequence labeling to assign entity tags (e.g., PERSON, ORG) to each token in a sentence.

• Sequence labelling: This process lets you label each token with a particular tag, such as a person's name. 
• Model: CRF (Conditional Random Fields) or model transformers, such as BERT, are typical models for NER. 
• Libraries: SpaCy and Hugging Face transformers offer pre-trained models to fine-tune outputs for NER tasks. 
• Use case: Some NER applications include social media monitoring and information retrieval to extract relevant entities for search optimization. 

6. What does syntactic analysis involve?

Syntactic analysis involves analyzing the structure of sentences to understand how words are connected. This step is crucial for breaking down sentence structure into grammatical relationships, helping machines understand the roles of words in context.

Techniques:

• Constituency parsing: Divides a sentence into hierarchical parts depending on syntactic rules, such as noun and verb phrases. 
• Dependency parsing: The technique focuses on how words are related to one another, such as subject-verb-object relationships. 

Applications:

• It enables you to understand the structure of a sentence and improve translation accuracy by preserving relationships between components. 
• It also helps you understand the relationship between entities in a text. 

Syntactic parsing can help you with complex tasks such as machine translation and summarization by understanding the grammatical structure before moving to semantics. 

7. What is semantic analysis, and how does it differ from syntactic analysis?

Semantic analysis involves extracting meaning from text based on context, while syntactic analysis focuses on the grammatical structure. While syntactic analysis provides the framework of relationships between words, semantic analysis looks deeper into the meaning those words convey.

Here’s a tabular format for addressing the difference between semantic and syntactic analysis:

8. What are the core components of an NLP system?

An NLP system is a sophisticated pipeline that transforms unstructured texts into structured, machine-understandable data. 

• The core components include tokenization, normalization, and removing stop words. In addition, stemming and lemmatization reduce the words to their root forms for data consistency. 
• Text representation is another component of NLP, where raw texts are converted into numerical vectors using techniques such as TF-IDF. 
• Word embeddings such as Word2Vec, GloVe, or FastText capture words' semantic meanings by mapping them to diverse vector spaces depending on their contexts. 
• At the modeling stages, ML algorithm components such as SVM or Random Forest are used for complex tasks such as classification. 
• Advanced deep learning models such as Long Short-Term Memory (LSTM) networks and transformer architectures are used for functions such as sequence learning. 

9. How are regular expressions used in text processing?

Regular expressions (regex) are powerful tools used in text processing to match patterns, search, and extract specific information from text. In NLP, regex is utilized during preprocessing to clean text data by matching and extracting patterns like email addresses, dates, or phone numbers.

Techniques:

• Pattern Matching: Detecting patterns in your provided text, such as identifying dates in the format DD-MM-YYYY.
• Substitution: Regex also allows for substituting specific patterns, such as replacing multiple spaces with a single space.
• Search and Extraction: Regex extracts specific elements, such as removing all email addresses from a document.

Libraries:

• Python re module: This module provides you with functions for matching and modifying strings, such as re.match(), re.findall(), and re.sub().
• SpaCy: Provides regex-based text matching tools to identify entities and patterns in your input.

Example:

import re
pattern = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'
matches = re.findall(pattern, text)

10. What is regular grammar in NLP?

Regular grammar in NLP is a formal grammar used to describe regular languages. It is beneficial for defining simple language structures that can be processed using finite state automata (FSA) or regular expressions. Regular grammar is typically applied in the lexical analysis phase of NLP, where simple syntactic structures are identified.

• Regular grammar can only define languages recognized by finite state machines (FSMs). 
• You can use this to define tokenization patterns or regular expression matching tasks. 

Use cases:

You can define the syntax of tokens, such as keywords, identifiers, and numbers, with the help of tokenization. Moreover, you can also extract simple patterns of information from data, such as phone numbers and dates. 

11. What are unigrams, bigrams, trigrams, and n-grams?

In NLP, unigrams, bigrams, trigrams, and n-grams refer to sequences of 'n' words in a text. These terms are used in text representation models like the Bag of Words (BoW) model to capture the frequency and relationships between words.

• Unigrams: A single word, for example, machine. 
• Bigrams: A sequence of two consecutive words, for example, machine learning. 
• Trigrams: It has three consecutive words, such as natural language processing. 
• N-grams: A general form of a sequence of n consecutive words. For example, 4-grams would capture sequences of four words. 

Techniques:

• BoW model: The technique uses n-grams to represent text by counting the occurrences of word sequences. 
• Collocation analysis: The process allows you to identify common word pairs or triplets using bigrams and trigrams. 

Use case:

To understand common word sequences, you can use n-grams for text classification. In addition, it is also applicable for speech recognition, where you can predict the next word or phrase depending on the previous n-grams. 

12. What is NLTK, and what are its key features?

The Natural Language Toolkit (NLTK) is a comprehensive library in Python used for NLP tasks such as text processing, tokenization, stemming, and more. NLTK is widely used for educational purposes and rapid prototyping in research and development.

Here are some of the key features of NLTK:

• Text Processing: Includes tools for tokenization, stemming, and part-of-speech tagging.
• Corpora and Datasets: NLTK provides access to several corpora, like WordNet and Treebank.
• Machine Learning: Supports classification and evaluation tasks using built-in datasets.
• Visualization Tools: NLTK also includes tools for visualizing parse trees and other data structures.

Example:

import nltk
from nltk.tokenize import word_tokenize
text = "Hello, how are you?"
tokens = word_tokenize(text)
print(tokens)

13. What is Part-of-Speech (POS) tagging?

Part-of-Speech (POS) tagging involves labeling each word in a sentence with its corresponding part of speech, such as noun, verb, adjective, and more. POS tagging helps identify the grammatical role of words, which is essential for tasks like syntactic analysis and machine translation.

• You can use this technique to tag algorithms like the Hidden Markov Model (HMM) or CRF. 
• POS tagging also helps you identify grammatical roles to improve overall translation accuracy. 
• It is also beneficial in NER processes, where you can differentiate between nouns and verbs to extract entities. 
• Libraries such as SpaCy and NLTK provide pre-trained POS taggers. 

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

Natural Language Processing (NLP) uses computational techniques to process and analyze human language. On the other hand, Natural Language Understanding (NLU) is a subset of NLP focused on interpreting the meaning of language, such as intent and relationships.

Here’s a comparison table between NLP and NLU:

15. What is the difference between NLP and Conversational AI?

NLP focuses on the general ability to process and analyze human language. Conversely, conversational AI refers to systems designed to simulate human conversation, such as chatbots and virtual assistants.

16. What is stemming? Provide an example.

Stemming is a text normalization process that reduces words to their root or base form. The goal is to remove suffixes from words. For example, running becomes run to standardize variations of a word.

• One of the most widely used stemming algorithms is the Porter stemmer. 
• Snowball stemmer, on the other hand, is an improved version of Porter’s algorithm. 

Use case:

Stemming helps reduce word variations in text classification and sentiment analysis tasks. However, you can encounter incorrect roots, making it a less accurate technique than lemmatization. 

17. What is lemmatization, and how does it improve over stemming?

Lemmatization is reducing words to their base or dictionary form (lemma) based on context, unlike stemming, which simply removes suffixes. Lemmatization uses vocabulary and morphological analysis to reduce words to their proper form.

• One of the significant techniques for lemmatization is the WordNet Lemmatizer in NLTK. 
• SpaCY also offers efficient lemmatization techniques using its dependency parsing. 
• Wordnet Lemmatizer is a popular algorithm based on the WordNet lexical database. It uses POS tags to determine the correct lemma. 
• A significant advantage of lemmatization is that it produces meaningful, dictionary-recognized words, whereas stemming can produce non-words, for example, better becomes bet. 

18. How can you tokenize a sentence using NLTK?

Tokenization is the process of splitting a sentence into words or sub-sentences. Using NLTK (Natural Language Toolkit), you can tokenize a sentence easily with the word_tokenize() function.

• Basic Tokenization: Involves splitting the text into individual words or sentences, making it easier for downstream processing like POS tagging, parsing, or named entity recognition.

Code example for using NLTK for word tokenization:

import nltk
from nltk.tokenize import word_tokenize
nltk.download('punkt')
sentence = "Natural Language Processing is fascinating!"
tokens = word_tokenize(sentence)
print(tokens)

Output:

['Natural', 'Language', 'Processing', 'is', 'fascinating', '!']

• Sentence Tokenization: NLTK also allows tokenization by sentences, which is helpful in tasks requiring sentence-level understanding.

Code example for using NLTK for sentence tokenization:

from nltk.tokenize import sent_tokenize
text = "Natural language processing is fun. Let's dive into it!"
sentences = sent_tokenize(text)
print(sentences)

Output:

['Natural language processing is fun.', "Let's dive into it!"]

Importance of tokenization:

• Tokenization is essential for text preprocessing tasks such as POS tagging, NER, and sentiment analysis. 
• It also helps you break down complex data into structured, analyzable units. 

Now, let’s look at some of the interview questions on NLP​ for aspiring professionals. 

Interview Questions on NLP for Aspiring Professionals

The section focuses on core concepts such as tokenization, text preprocessing, word embeddings such as Word2Vec, GloVe, and ML models like Naive Bayes and SVM. You will also gain insights into data preprocessing techniques such as stemming, lemmatization, and stop-word removal. 

Moreover, you will learn practical applications of these techniques using popular NLP libraries such as SpaCy and NLTK to solve common NLP challenges with Python. 

19. What is feature extraction in NLP?

Feature extraction in NLP converts raw text into a structured format that machine learning models can understand. The goal is identifying functional patterns or features to help the model make predictions or classifications. In NLP, feature extraction involves techniques like Bag of Words (BoW), TF-IDF, and word embeddings. 

• BoW converts the text you provide into a vector where each element represents the frequency of a word in the text.
• TF-IDF measures the importance of a word in your document relative to a corpus. 
• Word embeddings, for example, Word2Vec or GloVe, map words to high-dimensional vectors that capture semantic meaning. 

20. How does TF-IDF work, and why is it useful?

TF-IDF (Term Frequency-Inverse Document Frequency) is a statistical measure used to evaluate the importance of a word in a document relative to a collection (corpus) of documents. It is helpful in feature extraction because it highlights terms that are frequent in specific documents but rare across all documents in the corpus. 

• Term frequency: Term frequency allows you to measure a term's frequency in a document. It is calculated by the number of times a word appears in the document divided by the total number of words. 
• Inverse Document frequency (IDF): Measures the importance of a word across the entire corpus. Words that appear in fewer documents are considered more informative. The logarithmic scaling in IDF ensures that words appearing in nearly all documents are assigned a low IDF value. Moreover, rare words across the corpus are assigned higher IDF values.

The final TF-IDF score is calculated by multiplying a word's TF and IDF scores. This metric is widely used in tasks like text classification, information retrieval, and document clustering.

21. Explain Latent Semantic Indexing (LSI) with an example.

In NLP, Latent Semantic Indexing (LSI) is used to reduce the term-document matrix's dimensionality and uncover the text's hidden structure. LSI works by identifying patterns in the co-occurrence of words and mapping them to a latent semantic space.

• Create a term-document matrix where each row represents a word and each column represents a document.
• Apply Singular Value Decomposition (SVD) to decompose this matrix into smaller matrices that capture the underlying semantic relationships.
• The resulting matrix reveals hidden topics or concepts by associating similar terms that often appear together.

Latent Semantic Indexing (LSI) is a dimensionality reduction technique in NLP that helps uncover the hidden relationships between words in a corpus. LSI is designed to address the problem of synonymy and polysemy by capturing latent semantic structures from a term-document matrix.

Example: 

Imagine the words bank and river appear in similar contexts in different documents. In one document, bank refers to a financial institution, and in another, bank refers to the edge of a river. Traditional term-document matrices wouldn’t capture the relationship between these two uses of bank and their connection to words like money or financial institutions.

22. What are the essential steps in solving an NLP problem?

Solving an NLP problem involves several essential steps that transform raw, unstructured text into valuable insights or predictions.

Here’s a stepwise analysis for solving an NLP problem:

• Step 1: Problem definition: Your first step is understanding the risks, whether they involve classification, sentiment analysis, or NER.
• Step 2: Data Collection and Preprocessing: Clean the text by removing noise, tokenizing, stemming, or lemmatizing.
• Step 3: Feature Extraction: Convert the text into a numerical representation using techniques like TF-IDF or word embeddings.
• Step 4: Model Training: Train machine learning models, for example, SVM, Naive Bayes, and LSTM, on the preprocessed and vectorized data.
• Step 5: Model Evaluation: To evaluate a model's performance, you can use metrics like accuracy, precision, recall, and F1 score. 
• Step 6: Deployment: The final step is implementing the trained model in a real-time or batch production environment. 

Also read: Feature Selection in Machine Learning: Techniques, Benefits, and More.

23. What is pragmatic analysis in NLP, and where is it applied?

Pragmatics in NLP refers to studying how context influences the interpretation of meaning beyond the literal sense of the words. Pragmatic analysis involves understanding your intent and context for generating appropriate outputs. 

Applications:

• Dialogue Systems: Understanding user queries or requests in chatbots or virtual assistants.
• Sarcasm Detection: Identifying sarcastic remarks that do not match their literal meaning.
• Sentiment Analysis: Recognizing emotional undertones that go beyond simple word sentiment.

24. What is pragmatic ambiguity, and how do systems handle it?

Pragmatic ambiguity arises when the intended meaning of a statement is unclear due to its reliance on external context. For example, can you pass the salt? It could be a literal request or a polite command depending on the situation. Such examples involve indirect speech acts, where literal and intended meanings diverge.

How systems handle it:

• Contextual Embeddings: Modern models like BERT and GPT resolve pragmatic ambiguity by considering the entire context of the conversation, rather than just individual words.
• Reinforcement Learning: In conversational agents, systems learn to respond based on how you interact, refining their understanding of pragmatic meaning.

25. What is dependency parsing, and how does it improve text understanding?

Dependency parsing involves analyzing the grammatical structure of a sentence by establishing relationships between words. It creates a dependency tree where each word is connected to its syntactic governor, showing how words depend on each other in the sentence.

Improving text structure:

• It helps in machine translation, where understanding syntactic relationships ensures accurate translations.
• Enhances information extraction by identifying the subject, verb, and object in a sentence.
• Improves sentiment analysis by recognizing how sentiment-bearing words are related to the rest of the sentence.

Example:

In the sentence, the cat sat on the mat, dependency parsing links sat as the root verb, cat as the subject, and mat as the object of the preposition.

26. How can SpaCy be used to calculate word similarity?

SpaCy is a popular NLP library that offers pre-trained word vectors and utilities to calculate word similarity. It uses word embeddings to compute how similar two words are based on their vector representations in a high-dimensional space. Ensure words are in SpaCy's vocabulary, else similarity returns 0 or NaN.

Steps to calculate word similarity:

• The first step is to load the pre-trained Spacy model
• The next step is comparing two words using the .similarity() method. 

Note: Ensure words are in SpaCy's vocabulary; otherwise, similarity returns 0 or NaN.

Code Example:

import spacy
# Load pre-trained model
nlp = spacy.load('en_core_web_md')
# Define words
word1 = nlp('dog')
word2 = nlp('cat')
# Calculate similarity
similarity = word1.similarity(word2)
print(f"Similarity between 'dog' and 'cat': {similarity}")

In this example. The similarity score ranges from 0 to 1, where 1 means the words are identical and 0 means they are entirely different. 

27. What do precision and recall mean in NLP evaluation?

Precision and recall are two critical metrics used to evaluate the performance of an NLP model, especially in tasks like classification, NER, and information retrieval. These metrics help assess a model's performance in identifying relevant instances and avoiding false positives and negatives.

• Precision: Measures the accuracy of positive predictions made by the model. It is the ratio of true positives to the sum of true positives and false positives.
• Recall: Measures the ability of the model to capture all the positive cases. It is the ratio of true positives to the sum of true positives and false negatives.

Balancing both metrics is crucial, particularly in applications where false positives and negatives can have significant consequences, such as fraud detection or medical diagnostics.

28. What does the F1 Score represent, and when is it used?

The F1 Score is a performance metric used to evaluate the balance between precision and recall in classification models, between the positive and negative classes. The harmonic mean of precision and recall gives equal weight to both.

The F1 score is calculated as,

Use case:

You can use F1 scores in imbalanced datasets, where one class, for example. Fraud detection and disease diagnosis are much rarer. In such cases, optimizing for accuracy alone might be misleading. 

29. Can you list two typical real-world applications of NLP?

NLP is at the heart of many practical applications involving human and machine interaction through natural language.

• Voice assistants: One example is voice assistants such as Siri, Google Assistant, and Alexa, which rely on NLP to process user commands, enabling hands-free interactions. 
• Content moderation: Another key example is content moderation on social media platforms like Twitter, Facebook, and YouTube, where NLP techniques automatically detect and flag inappropriate content. 

These applications showcase how NLP technologies change user experiences across various industries by enhancing communication and content management.

Now, let’s look at some of the interview questions on NLP for experienced professionals. 

NLP  Interview Questions and Answers for Experienced Professionals

The section covers advanced NLP interview questions for experienced professionals, covering concepts such as transformer architectures and advanced model fine-tuning. These questions will provide insights into how to address complex challenges in text processing, language models, and evaluation metrics. 

You will also gain expertise in metrics such as precision, recall, FI-score in complex NLP tasks to use tools such as TensorFlow, Hugging Face, and more. 

30. How do you evaluate the performance of an NLP model in production?

Evaluating the performance of an NLP model in production extends beyond traditional offline metrics to include real-time monitoring and continuous feedback loops. In high-stakes environments, model performance tracking is vital for identifying potential degradation and ensuring sustained accuracy.

Metrics:

• Precision, Recall, and F1-score are crucial for classification tasks, measuring how accurately the model predicts positive and negative labels. However, in production, these metrics must be complemented with operational efficiency metrics such as Latency, Throughput, and Model Serving Time, measured through real-time data streams via tools like TensorFlow Serving or ONNX Runtime.
• BLEU and ROUGE are the standard metrics for machine translation and summarization tasks, capturing the n-gram overlap between generated text and reference text.
• You can use monitoring tools such as Prometheus and Grafana to capture live metrics and ensure the model’s predictions align with expected real-world outcomes. In real deployment, logging and monitoring systems are used to track predictions continuously across inputs, not just test sets. 

Model Evaluation in Production:

Continuous evaluation requires setting up A/B testing frameworks and tracking model drift through batch vs. real-time comparisons. Model retraining pipelines, orchestrated via KubeFlow or Apache Airflow, ensure that models are continuously updated with fresh data to maintain robustness in fluctuating environments.

Example code:

From sklearn.metrics import f1_score
import time
# Real-time prediction with logging
start_time = time.time()
y_pred = model.predict(X_test)
end_time = time.time()
print(f"Prediction Time: {end_time - start_time} seconds")
print(f"Real-Time F1 Score: {f1_score(y_test, y_pred)}")

Also read: Text Summarization in NLP: Techniques, Algorithms, and Real-World Applications

31. What metrics best suit different NLP tasks (e.g., classification, translation)?

In advanced NLP tasks, selecting the right evaluation metric for text classification ensures the model’s effectiveness and reliability in production.

Text classification:

• F1-score remains the most critical metric for imbalanced classes, where a simple accuracy score would be misleading. Precision and recall minimize false positives and negatives, particularly when working with sensitive applications like spam or fraud detection.
• Log-loss (cross-entropy loss) is frequently used in multiclass classification problems, particularly when dealing with softmax outputs.

Code example for text classification:

From sklearn.metrics import log_loss
# Assuming predicted probabilities and true labels
loss = log_loss(y_true, model.predict_proba(X_test))
print(f"Log-Loss: {loss}")

Machine translation:

• BLEU (Bilingual Evaluation Understudy) is widely used to evaluate the quality of machine-generated translations by comparing n-grams with reference translations.
• You can also use TER (Translation Edit Rate) in low-resource languages to measure the edits needed to match the reference translation.

Text Summarization:

• ROUGE (Recall-Oriented Understudy for Gisting Evaluation) measures the recall overlap between n-grams in a generated and reference summary. The ROUGE-L variant evaluates the longest common subsequence (LCS), crucial for assessing the semantic coherence of summaries.

Named Entity Recognition:

• Entry-level F1 and Exact Match (EM) are critical when you evaluate model entities, such as persons, organizations, and dates, in text with the high precision necessary for legal and financial documents. The Exact Match (EM) metric measures how many entities the model correctly identifies, with an exact match of the entity’s text span. 

32. What are the limitations or challenges of applying NLP in real-world use cases?

The application of NLP in use cases introduces several substantial challenges, such as data sparsity and scalability. These issues must be addressed to ensure models perform effectively and reliably under dynamic conditions.

• Polysemy and Ambiguity: Words like bank, bow, and lead can have multiple meanings based on context, which traditional word embeddings like Word2Vec or GloVe struggle to capture. You can use modern architectures like BERT and GPT-3 with contextual embeddings, understanding the word meanings based on sentence-level context.
• Data Sparsity: The success of deep learning models depends on massive annotated datasets. Insufficient data makes it challenging to train robust models for many languages, especially low-resource languages such as Swahili and Haitian Creole.
• Scalability in Production: NLP models like BERT are computationally expensive, requiring GPU acceleration and serving frameworks like ONNX or TensorFlow Lite for mobile devices. Moreover, efficient batch processing and real-time inference demand a high-performance computing infrastructure and careful model quantization.

Solution:

You can use multi-task learning and data augmentation techniques to address data sparsity and optimize models through knowledge distillation to reduce computational overhead in production. Knowledge distillation compresses a large ‘teacher’ model into a smaller ‘student’ model with minimal loss in accuracy. 

33. How is transfer learning applied in NLP using models like BERT or GPT?

Transfer learning in NLP uses pre-trained models like BERT and GPT to fine-tune them on task-specific datasets. It reduces the overall time and data needed to train models from scratch.

• BERT (Bidirectional Encoder Representations from Transformers) uses masked language modeling (MLM) for pre-training, allowing it to deeply understand each word's context in a sentence. It is particularly advantageous if you are working on tasks like question answering, sentence classification, and named entity recognition.

Fine-tuning example with BERT:

from transformers import BertForSequenceClassification, Trainer, TrainingArguments
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
trainer = Trainer(
    model=model,
    args=TrainingArguments(output_dir='./results'),
    train_dataset=train_data,
    eval_dataset=eval_data
)
trainer.train()

• GPT is considered suitable for fine-tuning, but OpenAI's GPT-3/4 is not open for traditional fine-tuning. These models use unsupervised pre-training on vast corpora and require much less data for fine-tuning compared to training from scratch.

34. What is Named Entity Disambiguation, and how is it handled?

Named Entity Disambiguation (NED) resolves the ambiguity between entities with the same name but different meanings, such as Apple Inc. and the fruit apple. This is essential in tasks like information retrieval and question answering.

Method:

• You can use BERT, RoBERTa, and XLNet, which utilize the transformer architecture to learn contextual information critical for disambiguation. 
• You can also use DBpedia, Wikidata, or Freebase, external links and models that can link entities to unique identifiers depending on additional data.  

Code example of disambiguation using BERT:Applications:

from transformers import BertTokenizer, BertForMaskedLM
# Load BERT tokenizer and pre-trained model
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForMaskedLM.from_pretrained('bert-base-uncased')
# Example sentence with ambiguous word 'Apple'
input_text = "I went to the Apple store yesterday."
input_ids = tokenizer.encode(input_text, return_tensors='pt')
# Get model predictions
outputs = model(input_ids)
predicted_token = tokenizer.decode(outputs[0][0][4].argmax())
# Output predicted word for 'Apple'
print(f"Predicted word for 'Apple': {predicted_token}")

You can use disambiguation for semantic search, chatbots, and information extraction, where the correct entity identification can improve the data's accuracy and relevance. 

35. What are contextual word embeddings, and how are they different from traditional embeddings like Word2Vec?

Contextual word embeddings are a type of word representation that varies depending on the context in which the word appears in a sentence. Unlike traditional word embeddings, such as Word2Vec, assigning a fixed vector to words, contextual embeddings use deep learning models like BERT and ELMo. 

36. How do advanced NLP models handle ambiguity and sarcasm in language?

Advanced NLP models like BERT, GPT-3, and other transformer-based architectures can handle ambiguities and subtle linguistic features such as sarcasm, irony, and context-dependent meanings. These models use deep contextual learning, attention mechanisms, and large-scale pre-training to infer meaning from the surrounding context. 

Handling ambiguity:

• Ambiguity arises when a word or phrase has multiple meanings depending on the context. For instance, bat can refer to a flying mammal or sports equipment. Traditional models like Word2Vec struggle with this since they map both meanings to the same vector. However, BERT handles ambiguity by learning bidirectional representations. The model uses the context to disambiguate the word’s meaning.

Example (Ambiguity with BERT):

from transformers import BertTokenizer, BertForMaskedLM
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForMaskedLM.from_pretrained('bert-base-uncased')
# Contextual word disambiguation with BERT
input_text = "I went to the bat house to check on the bat."
input_ids = tokenizer.encode(input_text, return_tensors='pt')
outputs = model(input_ids)
predicted_token = tokenizer.decode(outputs[0][0][6].argmax())  # Disambiguate "bat" based on context
print(f"Predicted word: {predicted_token}")

Contextual understanding with transformers:

• As a bidirectional transformer, BERT analyzes words before and after the target word. This allows it to differentiate meanings based on full sentence context rather than just the immediate surrounding words.
• GPT-3 takes a similar approach but uses a causal (unidirectional) transformer to generate text. This makes it capable of handling sentence-level context and ambiguity based on a broader input.

Handling Sarcasm:

Sarcasm involves a contrast between what is said and what is meant, which is difficult for traditional NLP models to understand. However, transformer-based models like GPT-3 and BERT can understand sarcasm due to their contextual learning capabilities.

Example (Sarcasm Detection with GPT-3):

import openai
openai.api_key = "your-api-key"
response = openai.Completion.create(
  engine="text-davinci-003",
  prompt="Oh great, another Monday!",
  max_tokens=50
)
print(response.choices[0].text.strip())  # Expected to detect sarcasm and respond appropriately

In this example, GPT-3 can detect that Oh great, another Monday! is likely sarcastic based on the context, even though it appears to express positivity.

Transformers and Sarcasm:

BERT and GPT-3 utilize their ability to capture context beyond the literal words, allowing them to interpret sarcasm. These models can infer that phrases like Oh great, another Monday! likely carry a negative sentiment, despite using positive wording.

Now let’s look at some strategies that can help you excel in interview questions on NLP.

 Top Strategies to Excel in NLP Interviews

Preparing for NLP interview questions involves a multi-faceted approach, ensuring you're well-versed in technical concepts and real-world applications of natural language processing. Each interview phase demands a different preparation strategy, from technical knowledge to aptitude skills and HR round questions. 

Technical Round Preparation

• Master Core Concepts: Ensure a strong foundation in machine learning, deep learning, and NLP-specific techniques like tokenization, word embeddings, TF-IDF, NER, semantic analysis, and dependency parsing. You should also consider transformer architecture fundamentals, which power models like RoBERTa, and DistilBERT.
• Learn Key NLP Libraries: Familiarize yourself with SpaCy, NLTK, Hugging Face Transformers, and TensorFlow for model development, training, and evaluation.
• Understand Algorithms and Models: Understand algorithms such as Naive Bayes, SVM, LSTMs, and BERT for various NLP tasks like classification, translation, and sentiment analysis. For more advanced and state-of-the-art NLP solutions, focus on deep learning models such as LSTMs, GRUs, GPT-3, and T5.
• Hands-on Coding Practice: Solve problems on platforms like LeetCode and HackerRank to practice implementing NLP algorithms in languages like Python, Java, or C++. Focus on coding tasks that involve text preprocessing (like tokenization and stemming), and implementing word embeddings (e.g., Word2Vec, GloVe). 

Aptitude Round Preparation

• Sharpen Problem-Solving Skills: Focus on data structures, algorithms, and logical reasoning problems. Understanding these will help you solve NLP-related puzzles effectively.
• Mathematical Foundation: You must understand the mathematics behind linear algebra, probability, and statistics, which are crucial for understanding the underlying models in NLP.
• Time Management: Practice solving problems under time constraints to build speed and accuracy, essential in aptitude rounds. Use timers during coding practice and solve five issues daily to simulate real-time pressure.

HR Round Preparation

• Prepare Your Story: Be ready to discuss your motivation for applying, your interest in NLP, and how your background aligns with the role.
• Behavioral Questions: Be prepared to answer common HR questions, such as Tell me about a time when you solved a problem or How do you deal with failure? With a focus on NLP-specific scenarios. For example, when asked about problem-solving, you could highlight a project where you successfully tackled a data preprocessing challenge.

Mock Interviews and Practice

• Simulate Real-World Interviews: Regularly engage in mock interviews to get used to the interview format. Use platforms like Pramp or Interviewing.io for free mock sessions.
• Code Under Pressure: Get used to coding on a whiteboard or in an online code editor, as some interviews might require you to solve problems in real time. In contrast, whiteboarding may be more common in traditional software engineering roles. In NLP interviews, you’ll likely be asked to code directly in an editor or solve text processing tasks like tokenization and TF-IDF implementation. 

Resume Building and Best Practices

• Highlight Key Projects: Ensure your resume highlights relevant NLP projects, especially if you've worked with transformer models, deep learning, or data preprocessing.
• Showcase Technical Skills: List all the programming languages, frameworks, and tools you are proficient in, such as Python, TensorFlow, SpaCy, PyTorch, AWS, Docker, and Kubernetes. Be sure to quantify your proficiency, like how you deployed an NLP model in a cloud environment (AWS) to handle millions of requests daily. 

Also read: 15+ Top Natural Language Processing Techniques To Learn in 2025

Get the Right NLP Interview Skills with upGrad

Once you’ve mastered the fundamentals of NLP, the next step is to apply this knowledge by working on real-world projects. Work on open-source NLP projects or contribute to GitHub to demonstrate your skills. Moreover, you can experiment with cloud-based tools like AWS Sagemaker or Google AI Platform for model deployment.

If you want to stay ahead of your peers with a solid understanding of NLP, look at upGrad’s courses that allow you to be future-ready. These are some of the additional courses that can help expand your entrepreneurial journey.

• Generative AI Foundations Certificate Program
• Generative AI Mastery Certificate for Data Analysis
• Generative AI Mastery Certificate for Software Development

Curious which courses can strengthen your NLP understanding for NLP interview questions? Contact upGrad for personalized counseling and valuable insights. For more details, you can also visit your nearest upGrad offline center. 

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Reference Link:
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