What does NLP stand for?
a) Natural Language Processing
b) Neural Language Programming
c) Network Language Processing
d) None of the above
Which of the following is a key task of Natural Language Processing?
a) Image recognition
b) Speech recognition
c) Data encryption
d) Temperature prediction
What is tokenization in NLP?
a) Converting text into numerical data
b) Breaking text into smaller units such as words or phrases
c) Encrypting text data
d) Combining words into a sentence
Which of the following is a common use of NLP?
a) Predicting the weather
b) Detecting fraud in transactions
c) Sentiment analysis
d) Classifying images
What is a “stopword” in NLP?
a) A word that holds a significant meaning in a text
b) A common word such as “the,” “is,” or “in,” often ignored in text analysis
c) A word that can be translated into multiple languages
d) A word used only in technical texts
Which of the following techniques is used to reduce words to their base or root form in NLP?
a) Stemming
b) Tokenization
c) Lemmatization
d) Part-of-speech tagging
What does a “bag of words” model represent in NLP?
a) The frequency of different parts of speech in a document
b) A representation where words are treated as independent and their order is ignored
c) A model that only analyzes sentence structure
d) A method for detecting named entities
Which of the following is an example of named entity recognition (NER)?
a) Recognizing the subject of a sentence
b) Identifying names of people, organizations, or locations in text
c) Finding the grammar rules of a language
d) Converting words into numerical vectors
What is a “word embedding” in NLP?
a) A method for finding synonyms
b) A way of converting words into fixed-length numerical vectors
c) A technique for removing stopwords from text
d) A model for translating languages
Which of the following is an example of a pre-trained NLP model?
a) Convolutional Neural Networks (CNN)
b) BERT (Bidirectional Encoder Representations from Transformers)
c) Long Short-Term Memory (LSTM)
d) Support Vector Machines (SVM)
What is “sentiment analysis” in NLP?
a) Identifying the grammatical structure of a sentence
b) Extracting the sentiment (positive, negative, or neutral) from text
c) Translating text from one language to another
d) Summarizing long documents
Which of the following is used in Part-of-Speech (POS) tagging in NLP?
a) To identify named entities in a text
b) To classify words into categories such as noun, verb, adjective, etc.
c) To convert words into their root forms
d) To detect the sentiment of the text
Which NLP technique helps in understanding the syntactic structure of a sentence?
a) Word segmentation
b) Dependency parsing
c) Tokenization
d) Named entity recognition
Which of the following is an example of a task that utilizes machine translation?
a) Translating a sentence from English to Spanish
b) Identifying spam emails
c) Classifying news articles into categories
d) Extracting keywords from text
What is the role of “part-of-speech tagging” in NLP?
a) Identifying the named entities in text
b) Assigning each word in a sentence to its grammatical role
c) Summarizing the content of a document
d) Converting text into numerical representations
What does the term “corpus” refer to in NLP?
a) A set of linguistic rules
b) A large collection of text used for training and analysis
c) The process of translating languages
d) A method for analyzing speech patterns
Which of the following is a characteristic of “LSTM” (Long Short-Term Memory) networks used in NLP?
a) They are used for image classification tasks
b) They can capture long-range dependencies in sequences
c) They are not suitable for sequential data
d) They only process fixed-length inputs
Which of the following NLP models is based on the Transformer architecture?
a) BERT
b) CNN
c) GAN
d) SVM
Which technique is used to find the most relevant words or phrases from a document in NLP?
a) Named entity recognition
b) Word embedding
c) Text summarization
d) Text ranking
What does “lemmatization” in NLP aim to achieve?
a) Removing punctuation from text
b) Converting words into their base or root form based on their meaning
c) Reversing the text to create a mirror image
d) Identifying the language of the text
Which of the following NLP techniques is used to find relationships between words in a sentence?
a) Sentiment analysis
b) Dependency parsing
c) Tokenization
d) Text classification
What is the primary goal of text classification in NLP?
a) To group words based on their meanings
b) To identify the language of a text
c) To assign predefined labels or categories to a text document
d) To identify keywords in a document
Which of the following NLP tasks involves summarizing long documents into shorter versions?
a) Named entity recognition
b) Text summarization
c) Language translation
d) Part-of-speech tagging
Which method is often used to represent words as numerical vectors in NLP?
a) Tokenization
b) Word embeddings
c) Lemmatization
d) POS tagging
What is “text generation” in NLP?
a) Creating new words from existing ones
b) Automatically generating text, such as in chatbots or content creation systems
c) Summarizing large documents
d) Translating text between languages
What does “semantic similarity” in NLP refer to?
a) The syntactic structure of two sentences
b) The number of common words between two texts
c) The degree to which two texts share the same meaning
d) The grammatical rules of a language
Which of the following is a common application of NLP in social media?
a) Image recognition
b) Sentiment analysis of posts and comments
c) Predicting user behavior
d) Audio transcription
What is the purpose of the “encoder-decoder” architecture in NLP?
a) To classify text into categories
b) To transform one sequence of data into another, such as in machine translation
c) To extract features from text
d) To convert text to speech
Which of the following is a common challenge in NLP?
a) Understanding handwriting
b) Handling ambiguous language and context
c) Calculating mathematical problems
d) Predicting stock prices
What is “language modeling” in NLP?
a) Predicting the next word in a sequence based on previous words
b) Understanding the meaning of individual words
c) Translating text between languages
d) Summarizing long documents
