Natural Language Processing (NLP): Key Techniques and Algorithms MCQ Exam
Test your knowledge of Natural Language Processing (NLP) with our MCQ exam on key techniques and algorithms. Explore concepts like tokenization, sentiment analysis and machine translation.
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1. Which of the following is a major task in Natural Language Processing (NLP)?
- Text classification
- Sentiment analysis
- Named entity recognition
- All of the above
2. What is the purpose of tokenization in NLP?
- To split text into individual words or phrases
- To identify the language of the text
- To assign labels to words
- To remove stop words from the text
3. Which of the following is a common technique used to represent words in a continuous vector space in NLP?
- One-hot encoding
- Word2Vec
- TF-IDF
- LSTM
4. What does the term "stemming" refer to in NLP?
- Extracting synonyms from a word
- Reducing words to their root forms
- Removing punctuation from text
- Identifying named entities in text
5. Which algorithm is commonly used for part-of-speech tagging in NLP?
- Naive Bayes
- Hidden Markov Model
- K-means clustering
- Support Vector Machine
6. Which of the following is NOT an example of a language model used in NLP?
- N-gram model
- Transformer model
- Word2Vec
- Random forest model
7. What is the key advantage of using a Transformer model in NLP?
- It can process text sequentially
- It can process long-range dependencies efficiently
- It works faster than traditional RNN models
- It uses a small number of layers
8. Which of the following is a method used to reduce the dimensionality of word representations in NLP?
- Word2Vec
- Latent Semantic Analysis (LSA)
- Long Short-Term Memory (LSTM)
- Decision trees
9. What is the function of the "attention mechanism" in a Transformer model?
- It focuses on specific parts of the input sequence while generating output
- It classifies the input sequence into predefined categories
- It filters out noisy data from the input
- It increases the size of the model
10. What is a key characteristic of a Recurrent Neural Network (RNN) in NLP?
- It processes input data in parallel
- It uses a loop to process sequences of data
- It is primarily used for image processing tasks
- It works with fixed-size input data
11. Which of the following techniques is commonly used for measuring the similarity between two pieces of text in NLP?
- Cosine similarity
- Jaccard similarity
- Euclidean distance
- All of the above
12. Which of the following is a commonly used NLP technique for sentiment analysis?
- Logistic regression
- Latent Dirichlet Allocation (LDA)
- Naive Bayes classifier
- K-means clustering
13. What does the term "word embeddings" refer to in NLP?
- Mapping words into a high-dimensional vector space
- A method to split text into individual words
- Removing punctuation from text
- A method for tokenizing text
14. Which of the following models is based on the idea of "self-attention" in NLP?
- LSTM
- Transformer
- CNN
- Naive Bayes
15. What does the "bag-of-words" model represent in NLP?
- A method for assigning weights to words based on their importance
- A technique to convert text into numerical form by counting word occurrences
- A method for splitting sentences into individual characters
- A model for representing the meaning of a sentence as a single vector
16. What is the purpose of using "TF-IDF" (Term Frequency-Inverse Document Frequency) in NLP?
- To convert words into one-hot vectors
- To find the most frequent words in a corpus
- To evaluate the importance of a word in a document relative to a corpus
- To create embeddings for words
17. Which of the following is a key challenge in NLP?
- Identifying the meaning of homonyms
- Handling large-scale image datasets
- Training models with small amounts of data
- Reducing computational resources
18. What is the purpose of using the "GloVe" (Global Vectors for Word Representation) model in NLP?
- To calculate word frequency
- To represent words as vectors in a continuous vector space
- To remove stop words from the text
- To split text into characters
19. Which of the following is a technique used to handle out-of-vocabulary (OOV) words in NLP?
- Using pre-trained word embeddings
- Tokenization
- Cross-validation
- Weight regularization
20. What is the purpose of "dependency parsing" in NLP?
- To identify the grammatical structure of a sentence and the relationships between words
- To convert text into word embeddings
- To split sentences into individual words
- To classify text into predefined categories
21. What is the main advantage of using a "pre-trained language model" like BERT in NLP tasks?
- It allows for faster training on small datasets
- It automatically processes sequences in parallel
- It improves performance on a variety of NLP tasks without task-specific training
- It requires less computational power
22. Which of the following is used to assess the relevance of a word in a document or corpus in NLP?
- TF-IDF
- Word2Vec
- K-means clustering
- Latent Dirichlet Allocation (LDA)
23. In NLP, what is the purpose of lemmatization?
- To remove stop words
- To reduce words to their dictionary form
- To convert all words to lowercase
- To split words into individual characters
24. In NLP, what is "named entity recognition" (NER) used for?
- Identifying named entities such as people, locations or organizations in text
- Classifying text into predefined categories
- Extracting sentiment from a piece of text
- Segmenting text into words
25. What is the role of "bigram" and "trigram" models in NLP?
- To capture the relationship between words in consecutive pairs (bigrams) or triplets (trigrams)
- To classify text into predefined categories
- To map words to fixed-length vectors
- To extract sentiment from text
26. Which algorithm is commonly used for text classification in NLP?
- Decision trees
- K-means clustering
- Support Vector Machine (SVM)
- Naive Bayes
27. Which of the following is a key challenge in machine translation in NLP?
- Handling word ambiguities and context-dependent meanings
- Reducing the dimensionality of word embeddings
- Training models with a large vocabulary
- Identifying sentence structure
28. What is a "collocation" in the context of NLP?
- A statistical measure of the importance of a word in a document
- A sequence of words that frequently occur together in a language
- A technique for reducing word vectors to a lower dimensionality
- A process of creating sentence-level embeddings
29. What does "language modeling" in NLP typically involve?
- Predicting the next word in a sequence of words based on context
- Reducing words to their root form
- Removing stop words from text
- Identifying named entities in a document
30. Which of the following techniques can be used for text generation in NLP?
- Sequence-to-sequence models
- Decision trees
- K-means clustering
- Principal Component Analysis (PCA)