1. What is tokenization in NLP?

A) The process of converting text to lowercase B) The process of breaking text into individual words or tokens C) The process of removing stop words from text D) The process of stemming words to their root form

2. What does "stemming" refer to in NLP?

A) Removing punctuation from text B) Reducing words to their root or base form C) Translating text from one language to another D) Converting text to numerical vectors

3. What is a Bag-of-Words model?

A) A model that considers the order of words in a sentence B) A model that represents text by the frequency of each word, ignoring grammar and order C) A model that uses neural networks to understand text D) A model that translates text into another language

4. What is the purpose of a stop word list in NLP?

A) To list all the words in a language B) To identify and remove common words that may not contribute significant meaning to text analysis C) To store synonyms of words D) To translate words from one language to another

5. Which of the following is a technique used for word embeddings?

A) TF-IDF B) One-hot encoding C) Word2Vec D) All of the above

6. What is Named Entity Recognition (NER) in NLP?

A) The process of translating text from one language to another B) The process of identifying and classifying key information (entities) in text into predefined categories C) The process of summarizing long documents into shorter versions D) The process of generating text based on a prompt

7. What does BERT stand for in NLP?

A) Basic Encoding and Retrieval Technique B) Bidirectional Encoder Representations from Transformers C) Binary Encoding and Recognition Technology D) Bidirectional Extraction of Representations and Tokens

8. What is the primary advantage of using Transformer models in NLP?

A) They process words sequentially, maintaining order B) They use convolutional layers to capture local features C) They leverage self-attention mechanisms to capture contextual relationships between words D) They are primarily used for speech recognition

9. What is sentiment analysis in NLP?

A) The process of extracting named entities from text B) The process of translating text from one language to another C) The process of determining the emotional tone behind words D) The process of generating human-like text

10. What is the purpose of the ROUGE metric in NLP?

A) To evaluate the performance of machine translation systems B) To measure the quality of summaries by comparing them to reference summaries C) To assess the accuracy of named entity recognition D) To evaluate the fluency of generated text