800+ NLP Interview Questions (Natural Language Processing)

800+ NLP Interview Questions (Natural Language Processing) udemy course free download

Master 800+ NLP Interview Questions: From Traditional Algorithms to Pre-Transformer Era with Detailed Explanations

Comprehensive NLP Interview Mastery

This intensive course provides complete preparation for Natural Language Processing interviews through 800+ carefully curated multiple-choice questions. Covering everything from foundational concepts to pre-transformer era, each question includes detailed explanations to ensure deep understanding rather than mere memorization.

Comprehensive Coverage Areas and Topics Included are :-

Complete NLP Study Guide - Pre-Transformer Era

I. Fundamentals of NLP (Difficulty: Easy to Medium)

1. Introduction to NLP (~30 MCQs)

Definition and Goals

• What is NLP? Why is it important?

History and Evolution

• Brief overview of symbolic, statistical, and neural approaches

Components of NLP

• NLU (Natural Language Understanding) vs. NLG (Natural Language Generation)

• Phases of NLP: morphological, lexical, syntactic, semantic, pragmatic analysis

Applications of NLP

• Text classification, sentiment analysis, machine translation (traditional)

• Chatbots (rule-based/statistical), information extraction

2. Text Preprocessing and Normalization (~100 MCQs)

Tokenization

• Word tokenization (NLTK's word_tokenize, spaCy's tokenizer)

• Sentence tokenization (NLTK's sent_tokenize)

• Handling punctuation, special characters, numbers

• Challenges: contractions, hyphenated words

Lowercasing

• Importance and impact

Stop Word Removal

• What are stop words? Why remove them?

• Common stop word lists (NLTK)

• Customizing stop word lists

Stemming

• Definition: Rule-based heuristic for reducing words to their root form

• Algorithms: Porter Stemmer, Lancaster Stemmer, Snowball Stemmer

• Limitations: Producing non-real words (e.g., "beautiful" → "beauti")

Lemmatization

• Definition: Reducing words to their base or dictionary form (lemma) using linguistic knowledge

• Comparison with Stemming: Advantages (more accurate, real words) and disadvantages (computationally more intensive)

• Tools: WordNetLemmatizer (NLTK), spaCy lemmatizer

Handling Special Characters and Noise

• Removing HTML tags, URLs, emojis

• Regular Expressions (RegEx) for pattern matching and cleaning

Character N-grams

• Concept and applications, particularly in handling OOV words

3. Text Representation (~120 MCQs)

One-Hot Encoding

• Concept and limitations: high dimensionality, sparsity, no semantic similarity

Bag-of-Words (BoW)

• Concept: Representing text as a multiset of its words, disregarding grammar and word order

• Creation process: Vocabulary, term frequency

• Limitations: Loss of word order/context, high dimensionality, sparsity

TF-IDF (Term Frequency-Inverse Document Frequency)

• Term Frequency (TF): How often a word appears in a document

• Inverse Document Frequency (IDF): Measures the importance of a word across a corpus

• Calculation: Formula and interpretation

• Applications: Information retrieval, keyword extraction

• Advantages over BoW

N-grams

• Unigrams, bigrams, trigrams, and higher-order n-grams

• Capturing local word sequences/context

• Applications: Language modeling, feature extraction for classification

• Sparsity issue with higher-order n-grams

Word Embeddings (Pre-LLM Era)

Concept: Dense vector representations of words capturing semantic and syntactic relationships

Word2Vec

• Skip-gram: Predicting context words from a target word

• CBOW (Continuous Bag-of-Words): Predicting a target word from its context words

• Training process, negative sampling, hierarchical softmax

GloVe (Global Vectors for Word Representation)

• Combining global matrix factorization and local context window methods

• Training objective

FastText

• Handling OOV words through character n-grams

• Learning embeddings for words and subwords

• Advantages for rare words and morphological rich languages

Cosine Similarity

• How to measure semantic similarity between word embeddings

Addressing Challenges with Embeddings

Handling Out-of-Vocabulary (OOV) Words (~20 MCQs)

Strategies:

• UNK token: Mapping all unknown words to a single "unknown" token

• Character-level embeddings: Representing words as sequences of characters, especially useful for morphologically rich languages or misspellings (FastText's approach)

• Subword tokenization (BPE, WordPiece, SentencePiece): Breaking words into sub-units to handle OOV and rare words

• Averaging pre-trained embeddings of constituent characters/subwords

• Using embeddings from a different but related domain

Custom Training Word Embeddings (~30 MCQs)

Why train custom embeddings?

• Domain-specific data: When pre-trained embeddings don't adequately capture semantics of words in specific domains (medical, legal, financial texts)

• Improving performance: Better representation for niche vocabulary

• Privacy/Data sensitivity: Training on private datasets

Process:

• Collecting a large, relevant corpus

• Choosing an embedding algorithm (Word2Vec, GloVe, FastText)

• Parameter tuning (embedding dimension, window size, negative sampling)

• Evaluating custom embeddings: Intrinsic (word similarity, analogy tasks) and Extrinsic (performance on downstream tasks)

Transfer Learning (basic concept): Using pre-trained embeddings as initialization and fine-tuning them on specific tasks/domains

Handling Missing Domain-Specific Data (~20 MCQs)

For Embeddings:

• Option 1: Train custom embeddings from scratch on domain-specific corpus

• Option 2: Fine-tune pre-trained embeddings on domain-specific corpus

• Option 3: Combine pre-trained and custom embeddings (concatenate or weighted average)

• Option 4: Character-level or subword-level embeddings (more robust to OOV and domain shift)

For Tokenizers (Pre-Transformer based):

• Rule-based customization: Adding specific rules for domain-specific acronyms, jargon, punctuation conventions

• Training a custom tokenizer: When domain's word formation rules are significantly different

• Lexicon-based tokenization: Using domain-specific lexicon to guide tokenization

II. Core NLP Tasks (Difficulty: Medium to Hard)

1. Text Classification (~80 MCQs)

Definition: Assigning predefined categories to text

Applications: Sentiment analysis, spam detection, topic labeling, intent recognition

Feature Engineering: Using BoW, TF-IDF, n-grams, word embeddings as features

Traditional Machine Learning Algorithms

Naive Bayes

• Bayes' Theorem for text classification

• Conditional independence assumption

• Multinomial Naive Bayes, Bernoulli Naive Bayes

• Add-one smoothing (Laplace smoothing)

Support Vector Machines (SVMs)

• Concept of hyperplane, margins, support vectors

• Kernel trick (linear, RBF)

• Suitability for high-dimensional text data

Logistic Regression

• Linear model for classification

• Sigmoid function

Evaluation Metrics

• Accuracy, Precision, Recall, F1-score

• Confusion Matrix

• ROC curve and AUC

2. Part-of-Speech (POS) Tagging (~50 MCQs)

Definition: Assigning a grammatical category (noun, verb, adjective) to each word in a sentence

Importance: Syntactic analysis, disambiguation, feature for other NLP tasks

Rule-based Tagging: Hand-crafted rules

Statistical Tagging

Hidden Markov Models (HMMs)

• States (POS tags), observations (words)

• Transition probabilities, emission probabilities

• Viterbi algorithm for finding the most likely tag sequence

Maximum Entropy (MaxEnt) Tagging

• Conditional probability models

• Feature functions for context

Evaluation: Tagging accuracy

3. Named Entity Recognition (NER) (~60 MCQs)

Definition: Identifying and classifying named entities (person names, organizations, locations, dates) in text

Applications: Information extraction, question answering, content summarization

Types of Named Entities

Rule-based Approaches: Pattern matching

Statistical Approaches

CRFs (Conditional Random Fields)

• Discriminative model for sequence tagging

• Advantages over HMMs (overcome independence assumption)

Feature Engineering for NER

• Word-level features (capitalization, suffixes, prefixes)

• Gazetteer features, part-of-speech tags

Evaluation: Precision, Recall, F1-score (using IOB/BIOES schemes)

4. Syntactic Parsing (~70 MCQs)

Definition: Analyzing the grammatical structure of sentences

Importance: Understanding sentence structure, machine translation, information extraction

Constituency Parsing (Phrase Structure Parsing)

• Building a parse tree (constituency tree) showing hierarchical phrase structures (NP, VP, PP)

• Context-Free Grammars (CFGs)

• CYK algorithm, Earley parser

Dependency Parsing

• Identifying grammatical relationships (dependencies) between words in a sentence (subject, object, modifier)

• Representing relationships as directed arcs between head and dependent words

• Types of dependencies ("nsubj", "dobj", "amod")

• Algorithms: Arc-eager, Arc-standard transition-based parsing

• Tools: spaCy, Stanford CoreNLP

Ambiguity in Parsing: Attachment ambiguity, coordination ambiguity

5. Semantic Analysis (~70 MCQs)

Definition: Understanding the meaning of words, sentences, and texts

Word Sense Disambiguation (WSD)

• Definition: Identifying the correct meaning of a word in a given context ("bank" - financial institution vs. river bank)

• Approaches: Supervised (using sense-tagged corpora), Unsupervised (using context similarity)

Semantic Role Labeling (SRL)

• Identifying the semantic roles of constituents in a sentence (Agent, Patient, Instrument)

• FrameNet, PropBank

Coreference Resolution

• Definition: Identifying all expressions in a text that refer to the same entity ("John" and "he" referring to the same person)

• Anaphora Resolution

• Applications: Document summarization, question answering

Lexical Semantics: Synonyms, antonyms, hyponyms, hypernyms

Distributional Semantics: Words appearing in similar contexts have similar meanings (foundation for word embeddings)

6. Machine Translation (Traditional) (~50 MCQs)

Rule-Based Machine Translation (RBMT)

• Linguistic rules for grammar, syntax, and semantics

• Limitations: High development cost, difficulty in covering all linguistic phenomena

Statistical Machine Translation (SMT)

• Concept: Translating based on statistical models learned from parallel corpora

• Noisy Channel Model: P(source | target) = P(target | source) * P(source)

• Components: Language model, translation model, distortion model

• Phrase-based SMT

• Limitations: Requires large parallel corpora, ignores long-range dependencies

Evaluation Metrics: BLEU (Bilingual Evaluation Understudy) score

7. Text Summarization (~40 MCQs)

Definition: Creating a concise and coherent summary of a given text

Types

Extractive Summarization

• Identifying and extracting important sentences/phrases from the original text

• Techniques: TF-IDF based scoring, TextRank, LexRank

Abstractive Summarization

• Generating new sentences that capture the main ideas of the original text (more complex, closer to NLG)

• Early approaches used rule-based systems

Evaluation Metrics: ROUGE (Recall-Oriented Understudy for Gisting Evaluation) score

8. Information Retrieval and Search (~30 MCQs)

Concept: Finding relevant information from a large collection of documents

Indexing: Inverted index

Ranking: Using TF-IDF, Cosine Similarity

Boolean Retrieval: Exact match

Vector Space Model: Representing documents and queries as vectors

9. Sentiment Analysis (~50 MCQs)

Definition: Determining the emotional tone or sentiment (positive, negative, neutral) of a piece of text

Levels: Document-level, sentence-level, aspect-level

Approaches

Lexicon-based

• Using sentiment lexicons (word lists with sentiment scores)

• Rule-based methods (counting positive/negative words)

• Handling negation, intensifiers

Machine Learning-based

• Feature engineering (n-grams, POS tags, sentiment scores from lexicons)

• Traditional ML algorithms (Naive Bayes, SVM, Logistic Regression)

Challenges: Sarcasm, irony, context dependency, handling "not"

Evaluation: Precision, Recall, F1-score

III. Introduction to Neural Networks for NLP (Pre-Transformer/LLM) (Difficulty: Medium to Hard)

1. Basic Neural Networks (~30 MCQs)

Perceptron, Multi-Layer Perceptron (MLP)

• Activation functions (Sigmoid, ReLU, Tanh)

• Feedforward networks

• Backpropagation algorithm

Loss Functions: Cross-entropy

Optimizers: Gradient Descent, Stochastic Gradient Descent (SGD), Adam

2. Recurrent Neural Networks (RNNs) (~70 MCQs)

Concept: Handling sequential data

Architecture: Hidden state, recurrence

Challenges

• Vanishing Gradients: Difficulty in learning long-range dependencies

• Exploding Gradients: Gradients becoming too large

Applications: Language modeling (next word prediction), sequence tagging (POS, NER)

3. Long Short-Term Memory (LSTM) Networks (~60 MCQs)

Motivation: Addressing vanishing gradients in RNNs

Architecture: Cell state, input gate, forget gate, output gate

Functionality: How gates control information flow

4. Gated Recurrent Units (GRUs) (~40 MCQs)

Motivation: Simpler alternative to LSTMs

Architecture: Reset gate, update gate

Comparison with LSTMs: Fewer parameters, sometimes comparable performance

5. Encoder-Decoder Architecture (Pre-Attention) (~40 MCQs)

Concept: Encoding source sequence into a fixed-length context vector, then decoding into target sequence

Applications: Machine Translation, Text Summarization

Limitations: Fixed-length context vector bottleneck for long sequences

IV. Practical Aspects and Evaluation (Difficulty: Medium)

1. NLP Libraries and Tools (~30 MCQs)

NLTK (Natural Language Toolkit)

• Strengths: Comprehensive, good for learning and research, includes many linguistic resources

• Common functionalities: Tokenization, stemming, lemmatization, POS tagging, parsing

spaCy

• Strengths: Production-ready, fast, efficient, good for industrial applications

• Common functionalities: Tokenization, NER, dependency parsing, word vectors (pre-trained)

Gensim

• Strengths: Topic modeling (LDA, LSI), word embeddings (Word2Vec, Doc2Vec)

Scikit-learn

• Strengths: Machine learning algorithms for text classification

• CountVectorizer, TfidfVectorizer

2. Model Evaluation (~30 MCQs)

General ML Metrics: Precision, Recall, F1-score, Accuracy, AUC-ROC

Task-Specific Metrics

• BLEU for Machine Translation

• ROUGE for Text Summarization

• Perplexity for Language Models

Cross-Validation: K-fold, stratified

Overfitting and Underfitting: Concepts and mitigation strategies

3. Data Annotation and Dataset Curation (~20 MCQs)

• Importance of high-quality annotated data

• Common annotation guidelines (IOB format for NER)

• Challenges in data collection and annotation

4. Ethical Considerations in NLP (~10 MCQs)

• Bias in data and models

• Fairness, accountability, transparency

• Privacy concerns

And Much More !!!

This comprehensive guide covers traditional and neural methods from the pre-Transformer era, with particular emphasis on handling out-of-vocabulary words, custom embedding training, and domain-specific data challenges.