1. Which of the following is a potential ethical concern related to the use of NLP in automated systems?
A. Increased computational power requirements.
B. Potential for bias in NLP models leading to unfair or discriminatory outcomes.
C. Reduced accuracy in text summarization tasks.
D. Difficulty in processing multilingual text.
2. What is the role of 'syntax′ in natural language?
A. The meaning of words and phrases.
B. The structure and grammatical rules of sentences.
C. The pronunciation of words.
D. The social context of language use.
3. Which approach to NLP relies on explicitly programmed linguistic rules?
A. Machine Learning-based NLP
B. Rule-based NLP
C. Statistical NLP
D. Deep Learning-based NLP
4. Which of these techniques is used to reduce words to their root form, considering the word′s meaning and context?
A. Stemming
B. Lemmatization
C. Tokenization
D. Stop word removal
5. What is a potential drawback of 'stemming′ compared to 'lemmatization′?
A. Stemming is computationally more expensive.
B. Stemming may result in stems that are not actual words, reducing readability and interpretability.
C. Lemmatization is less effective in reducing words to their root form.
D. Stemming requires more linguistic knowledge.
6. What is the purpose of 'stop word removal′ in NLP?
A. To identify the most important words in a text.
B. To convert words to their root form.
C. To eliminate common words that usually do not contribute significantly to the meaning.
D. To correct spelling errors in a text.
7. Sentiment analysis primarily aims to determine:
A. The topic of a given text.
B. The author of a given text.
C. The emotional tone or attitude expressed in a text.
D. The grammatical correctness of a given text.
8. If an NLP model consistently misclassifies text from a particular demographic group, this is primarily an issue of:
A. Overfitting
B. Bias
C. Variance
D. Low precision
9. What is 'language modeling′ in NLP?
A. The process of translating text from one language to another.
B. The task of predicting the next word in a sequence.
C. The process of identifying named entities in text.
D. The task of summarizing long documents.
10. Which NLP task involves converting spoken language into written text?
A. Text-to-Speech (TTS)
B. Speech Recognition (ASR)
C. Machine Translation (MT)
D. Text Summarization
11. Which of the following is an example of 'semantic ambiguity′ in natural language?
A. The word 'bank′ can refer to a financial institution or the side of a river.
B. The sentence 'Colorless green ideas sleep furiously′ is grammatically correct but nonsensical.
C. Pronouncing 'read′ differently based on tense (present vs. past).
D. Using slang terms that are not universally understood.
12. What is 'parsing′ in NLP?
A. The process of generating text from structured data.
B. The process of analyzing the syntactic structure of a sentence to determine its grammatical relationships.
C. The process of identifying the topic of a text.
D. The process of translating text from one language to another.
13. What is 'tokenization′ in NLP?
A. The process of converting text to speech.
B. The process of breaking down text into smaller units, such as words or phrases.
C. The process of identifying named entities in text.
D. The process of removing stop words from text.
14. In NLP, what does 'n-gram′ refer to?
A. A type of neural network architecture.
B. A sequence of 'n′ words in a text.
C. A method for named entity recognition.
D. A metric for evaluating machine translation quality.
15. Consider a chatbot designed for customer service. Which NLP task is MOST critical for its initial interaction with a user?
A. Sentiment Analysis
B. Named Entity Recognition
C. Intent Recognition
D. Text Summarization
16. What is 'pragmatics′ in linguistics and NLP?
A. The study of word formation and structure.
B. The study of sentence structure and grammar.
C. The study of the meaning of words and sentences in context.
D. The study of speech sounds.
17. What is a 'corpus′ in NLP?
A. A rule-based system for language processing.
B. A collection of text documents used for training NLP models.
C. An algorithm for sentiment analysis.
D. A technique for text summarization.
18. What is the 'attention mechanism′ in Transformer networks?
A. A method to reduce the dimensionality of word embeddings.
B. A technique that allows the model to focus on different parts of the input sequence when processing each word.
C. A way to convert text into speech.
D. A type of recurrent layer in neural networks.
19. In the context of chatbots, what is 'intent recognition′?
A. Identifying the language in which the user is communicating.
B. Understanding the user′s goal or purpose behind their message.
C. Generating a response to the user′s message.
D. Storing the conversation history.
20. What is the primary goal of Natural Language Processing (NLP)?
A. To translate human languages into programming code.
B. To enable computers to understand, interpret, and manipulate human language.
C. To create artificial languages for machine communication.
D. To analyze the statistical properties of large datasets.
21. Which of these NLP models is particularly well-suited for processing sequential data like text, due to its memory capabilities?
A. Convolutional Neural Network (CNN)
B. Recurrent Neural Network (RNN)
C. Feedforward Neural Network
D. Support Vector Machine (SVM)
22. In the context of Machine Translation, what does BLEU score measure?
A. The grammatical correctness of the translated text.
B. The fluency of the translated text.
C. The similarity of the translated text to reference translations.
D. The speed of the translation process.
23. What is a key advantage of using Transformer networks over RNNs for many NLP tasks?
A. Transformers are better at handling very long sequences and can be parallelized.
B. RNNs are computationally more efficient than Transformers.
C. RNNs are better at capturing long-range dependencies in text.
D. Transformers are simpler to train than RNNs.
24. What is the purpose of 'text summarization′ in NLP?
A. To translate text into another language.
B. To shorten a text while retaining its most important information.
C. To analyze the sentiment expressed in a text.
D. To identify the grammatical structure of a text.
25. What is 'topic modeling′ in NLP?
A. The process of translating text into different languages.
B. A technique to discover abstract topics in a collection of documents.
C. The process of identifying named entities in text.
D. A method for evaluating machine translation quality.
26. What is the purpose of 'word embeddings′ like Word2Vec and GloVe in NLP?
A. To count the frequency of words in a text.
B. To represent words as dense vectors in a continuous vector space, capturing semantic relationships.
C. To translate words into different languages.
D. To correct spelling errors in words.
27. Which of the following NLP tasks is concerned with identifying the grammatical role of each word in a sentence, such as noun, verb, adjective, etc.?
A. Named Entity Recognition
B. Sentiment Analysis
C. Part-of-Speech Tagging
D. Text Summarization
28. Which NLP technique is used to identify real-world objects, such as persons, locations, and organizations, in text?
A. Topic Modeling
B. Named Entity Recognition (NER)
C. Text Summarization
D. Machine Translation
29. Which of the following is NOT a common application of NLP?
A. Spam email detection
B. Image recognition
C. Chatbots
D. Machine translation
30. What is the main challenge that 'polysemy′ poses in NLP?
A. Dealing with words that are spelled incorrectly.
B. Handling words that have multiple meanings.
C. Processing text in multiple languages.
D. Understanding the emotional tone of text.
