NSCT – Natural Language Processing (NLP) MCQs 20 min Score: 0 Attempted: 0/20 Subscribe 1. . Natural Language Processing (NLP) is: (A) Compressing datasets only (B) Encrypting text data only (C) A branch of AI that focuses on the interaction between computers and human language (D) Backup onlyShow All Answers 2. . Tokenization in NLP refers to: (A) Encrypting tokens (B) Splitting text into smaller units like words or sentences (C) Compressing text (D) Backup only 3. . Stemming in NLP is: (A) Backup only (B) Encrypting words (C) Compressing words (D) Reducing words to their root form (e.g., "running" → "run") 4. . Lemmatization differs from stemming because it: (A) Backup only (B) Encrypts words (C) Compresses words (D) Converts words to their base or dictionary form considering context 5. . Stop words in NLP are: (A) Compressing words (B) Encrypting words (C) Commonly used words like "the", "is", "and" which are often removed during preprocessing (D) Backup only 6. . Bag-of-Words (BoW) is: (A) Backup only (B) Encrypting words (C) Compressing text (D) A representation of text as a collection of word frequencies ignoring grammar and order 7. . TF-IDF stands for: (A) Term Frequency-Inverse Document Frequency, used to weigh word importance (B) Encrypting words (C) Compressing features (D) Backup only 8. . Word embeddings like Word2Vec or GloVe are used to: (A) Represent words as dense vectors capturing semantic meaning (B) Encrypt vectors (C) Compress vectors (D) Backup only 9. . Named Entity Recognition (NER) is: (A) Compressing text (B) Encrypting entities (C) Identifying and classifying entities like names, locations, and dates in text (D) Backup only 10. . Part-of-Speech (POS) tagging assigns: (A) Encrypts POS tags (B) Grammatical labels such as noun, verb, adjective to words in a sentence (C) Compresses tags (D) Backup only 11. . Sentiment analysis in NLP aims to: (A) Encrypt text (B) Determine the emotion or opinion expressed in text (C) Compress data (D) Backup only 12. . Sequence-to-Sequence (Seq2Seq) models are used for: (A) Machine translation, text summarization, and chatbots (B) Encrypting sequences (C) Compressing sequences (D) Backup only 13. . Attention mechanism in NLP allows: (A) Compressing sequences (B) Encrypting attention (C) The model to focus on relevant parts of the input sequence for better predictions (D) Backup only 14. . Transformers are: (A) Encrypting transformers (B) Deep learning models based entirely on attention mechanisms for NLP tasks (C) Compressing models (D) Backup only 15. . BERT is: (A) Encrypting BERT (B) A pre-trained transformer model for understanding context in NLP tasks (C) Compressing BERT (D) Backup only 16. . GPT models are used for: (A) Encrypting text (B) Text generation, summarization, and conversational AI (C) Compressing text (D) Backup only 17. . Text preprocessing in NLP typically includes: (A) Backup only (B) Encrypting text (C) Compressing data (D) Tokenization, stop word removal, stemming, lemmatization, and normalization 18. . Cosine similarity in NLP is used to: (A) Compress similarity (B) Encrypt similarity (C) Measure similarity between two text vectors (D) Backup only 19. . N-grams in NLP represent: (A) Backup only (B) Encrypting sequences (C) Compressing sequences (D) Contiguous sequences of N words used for text modeling 20. . The main purpose of NLP is to: (A) Enable machines to understand, interpret, and generate human language effectively (B) Encrypt all text (C) Compress datasets (D) Backup only
