technology
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10 sample questions
Natural Language Processing MCQ Practice Test
Text analysis, chatbots, and language understanding
Q1. Which NLP task involves identifying and extracting specific entities such as names, locations, and organizations from text?
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A. Text Classification
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B. Named Entity Recognition (NER) ✓
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C. Part-of-Speech (POS) Tagging
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D. Sentiment Analysis
Explanation: Named Entity Recognition (NER) is a subfield of NLP that deals with identifying and categorizing named entities in unstructured text into predefined categories such as names, locations, and organizations.
Q2. Which natural language processing (NLP) technique is used to identify the sentiment of a piece of text as positive, negative, or neutral?
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A. Part-of-Speech (POS) tagging
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B. Named Entity Recognition (NER)
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C. Sentiment Analysis ✓
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D. Dependency Parsing
Explanation: Sentiment Analysis is a technique used in NLP to determine the emotional tone or attitude conveyed by a piece of text. It can identify whether the text is positive, negative, or neutral.
Q3. What is the primary function of the WordPiece tokenization technique used in BERT?
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A. It breaks down words into subwords to handle out-of-vocabulary words ✓
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B. It replaces words with their synonyms to improve semantic understanding
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C. It uses a dictionary to map words to their corresponding IDs
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D. It applies stemming to reduce words to their base form
Explanation: WordPiece tokenization is a technique used in BERT to handle out-of-vocabulary words. It breaks down words into subwords, which are then used to represent the original word in the model.
Q4. What is the primary goal of the Bag-of-Words (BoW) representation in Natural Language Processing?
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A. to capture the semantic meaning of words
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B. to represent text as a numerical vector ✓
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C. to preserve the word order in a sentence
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D. to remove stop words and punctuation
Explanation: The Bag-of-Words (BoW) representation is a common technique in Natural Language Processing that represents text as a numerical vector. This is achieved by counting the frequency of each word in the text and storing it in a vector. This allows for efficient comparison and analysis of text data.
Q5. Which of the following natural language processing techniques is primarily used to analyze the grammatical structure of a sentence and understand the relationships between words?
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A. Named Entity Recognition (NER)
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B. Part-of-Speech (POS) Tagging
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C. Dependency Parsing ✓
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D. Word Embeddings
Explanation: Dependency Parsing analyzes the grammatical structure of a sentence to identify the relationships between words (e.g., subject-verb-object). This analysis helps computers understand how words relate to each other within a sentence, which is crucial for understanding the overall meaning and context.
Q6. In Natural Language Processing, which algorithm is used to convert text data into numerical vectors that can be processed by machine learning algorithms?
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A. K-Means Clustering
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B. Principal Component Analysis (PCA)
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C. Word2Vec ✓
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D. Decision Trees
Explanation: Word2Vec is a technique used in NLP to convert words into numerical vectors, allowing them to be processed by machine learning algorithms. This is particularly useful for tasks such as text classification and clustering.
Q7. What is the primary function of the Tokenizer component in a Natural Language Processing (NLP) pipeline?
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A. It generates a list of possible word senses for a given input word.
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B. It breaks down text into individual words or tokens, which are then processed by other NLP components. ✓
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C. It is responsible for named entity recognition (NER) and part-of-speech (POS) tagging.
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D. It is a machine learning model that generates human-like text based on a given prompt.
Explanation: The Tokenizer component is a crucial part of an NLP pipeline, as it prepares the input text for further processing by breaking it down into individual words or tokens. This allows other components, such as word embeddings and sentiment analysis, to operate on the text effectively.
Q8. What is the primary goal of the WordPiece tokenization technique used in BERT?
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A. to increase the vocabulary size of the model
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B. to reduce the dimensionality of the input data
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C. to represent subwords as single tokens ✓
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D. to improve the model's ability to handle out-of-vocabulary words
Explanation: WordPiece tokenization is a technique used in BERT to split words into subwords, which are then represented as single tokens. This allows the model to handle out-of-vocabulary words and improve its overall performance.
Q9. Which tokenization technique is used in the NLTK library to split text into words and punctuation?
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A. Wordpiece tokenization
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B. Subword tokenization
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C. Word-level tokenization ✓
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D. Sentence-level tokenization
Explanation: The NLTK library uses the word-level tokenization technique to split text into individual words and punctuation marks. This is in contrast to other techniques like wordpiece or subword tokenization, which are used in other libraries like BERT and WordPieceTokenizer.
Q10. What is the primary goal of the Named Entity Recognition (NER) task in Natural Language Processing?
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A. Part-of-speech tagging
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B. Sentiment analysis
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C. Extracting and categorizing named entities ✓
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D. Text summarization
Explanation: Named Entity Recognition (NER) is a task in Natural Language Processing that focuses on identifying and categorizing named entities in unstructured text into predefined categories such as person, organization, location, etc.
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