Q.1 What does NLP stand for in Artificial Intelligence?
Natural Logic Processing
Natural Language Processing
Neural Linguistic Programming
Non-Linear Processing
Explanation - NLP stands for Natural Language Processing, which is the field of AI focused on enabling computers to understand, interpret, and generate human language.
Correct answer is: Natural Language Processing
Q.2 Which of the following is an application of NLP?
Image recognition
Speech recognition
Language translation
Object detection
Explanation - Machine translation, such as Google Translate, is a key application of NLP.
Correct answer is: Language translation
Q.3 Tokenization in NLP refers to:
Converting text to speech
Splitting text into words or sub-units
Removing stopwords
Encoding sentences into vectors
Explanation - Tokenization is the process of breaking down text into smaller units like words or subwords.
Correct answer is: Splitting text into words or sub-units
Q.4 Which of the following is a stopword in English?
Computer
The
Language
Machine
Explanation - Stopwords are common words such as 'the', 'is', 'and' that often carry little meaning in analysis.
Correct answer is: The
Q.5 What is lemmatization in NLP?
Reducing a word to its root form
Removing punctuation
Detecting sentence boundaries
Word frequency analysis
Explanation - Lemmatization reduces words to their base or dictionary form, e.g., 'running' → 'run'.
Correct answer is: Reducing a word to its root form
Q.6 Which model introduced the concept of 'attention mechanism' in NLP?
Word2Vec
Seq2Seq
Transformer
Naive Bayes
Explanation - The Transformer model introduced attention, which significantly improved performance in NLP tasks.
Correct answer is: Transformer
Q.7 Which of these is an NLP library in Python?
NumPy
NLTK
OpenCV
Matplotlib
Explanation - NLTK (Natural Language Toolkit) is a Python library for NLP tasks.
Correct answer is: NLTK
Q.8 Which of the following tasks involves assigning grammatical categories to words?
NER
POS tagging
Sentiment analysis
Parsing
Explanation - Part-of-speech (POS) tagging assigns words to categories like noun, verb, adjective.
Correct answer is: POS tagging
Q.9 Which algorithm is commonly used for text classification?
K-Means
Naive Bayes
KNN
Apriori
Explanation - Naive Bayes is simple yet effective for text classification tasks.
Correct answer is: Naive Bayes
Q.10 What does 'Bag of Words' represent in NLP?
Syntax structure
Word order
Word frequency counts
Sentence embeddings
Explanation - Bag of Words model represents text as frequency counts of words, ignoring word order.
Correct answer is: Word frequency counts
Q.11 Which task is related to finding names of people, places, and organizations in text?
POS tagging
NER
Parsing
Sentiment analysis
Explanation - Named Entity Recognition (NER) identifies entities like people, locations, and organizations.
Correct answer is: NER
Q.12 Which of the following is an embedding technique for words?
Word2Vec
SVM
Random Forest
KNN
Explanation - Word2Vec is a popular word embedding method that maps words to continuous vectors.
Correct answer is: Word2Vec
Q.13 Which neural network is often used for sequence modeling in NLP?
CNN
RNN
GAN
DBN
Explanation - Recurrent Neural Networks (RNNs) are suitable for sequential data such as text.
Correct answer is: RNN
Q.14 What is the purpose of stemming in NLP?
Finding synonyms
Reducing words to their root form
Tokenizing text
Generating embeddings
Explanation - Stemming cuts words to their base forms, e.g., 'studies' → 'studi'.
Correct answer is: Reducing words to their root form
Q.15 Which of these models is used in modern NLP for contextual embeddings?
Word2Vec
TF-IDF
BERT
Naive Bayes
Explanation - BERT provides deep contextual embeddings by considering both left and right context.
Correct answer is: BERT
Q.16 In TF-IDF, what does 'IDF' stand for?
Inverse Document Frequency
Important Data Factor
Index Document Factor
Internal Data Frequency
Explanation - TF-IDF combines Term Frequency and Inverse Document Frequency to weigh word importance.
Correct answer is: Inverse Document Frequency
Q.17 Which metric is often used to evaluate machine translation quality?
BLEU score
Accuracy
F1 score
Recall
Explanation - BLEU (Bilingual Evaluation Understudy) score is used to evaluate translation systems.
Correct answer is: BLEU score
Q.18 Word sense disambiguation in NLP deals with:
Finding grammar errors
Determining correct meaning of words in context
Splitting text into sentences
Generating synonyms
Explanation - Word sense disambiguation identifies which meaning of a word is used in a given context.
Correct answer is: Determining correct meaning of words in context
Q.19 Which of the following is a pre-trained NLP model?
ResNet
BERT
VGG
AlexNet
Explanation - BERT is a transformer-based pre-trained NLP model widely used for language tasks.
Correct answer is: BERT
Q.20 Sentiment analysis is a task in NLP that focuses on:
Topic classification
Opinion mining
Speech recognition
Parsing sentences
Explanation - Sentiment analysis detects emotions, opinions, or attitudes in text data.
Correct answer is: Opinion mining
Q.21 Which is an unsupervised method for topic modeling?
LDA
Naive Bayes
Logistic Regression
Decision Trees
Explanation - Latent Dirichlet Allocation (LDA) is an unsupervised learning technique for topic modeling.
Correct answer is: LDA
Q.22 What does OOV stand for in NLP?
Out of Vocabulary
Order of Variation
Optimal Output Vector
Overlapping Occurrence Values
Explanation - OOV refers to words not present in the training vocabulary of an NLP model.
Correct answer is: Out of Vocabulary
Q.23 Which component is used in Transformers for parallel sequence processing?
Convolution
Recurrent units
Attention mechanism
Pooling
Explanation - Transformers rely on attention mechanisms instead of recurrence for processing sequences.
Correct answer is: Attention mechanism
Q.24 What is a language model?
A model for predicting word sequences
A model for recognizing objects
A model for summarizing videos
A model for clustering images
Explanation - Language models predict the probability of word sequences and are essential in NLP tasks.
Correct answer is: A model for predicting word sequences
Q.25 Which of these is an example of a dialogue system?
Google Translate
Siri
Photoshop
TensorFlow
Explanation - Siri is a conversational AI system that interacts with users using NLP.
Correct answer is: Siri