Natural Language Processing Trivia 3 — Questions and Answers
Question 1: Which NLP task assigns grammatical roles like noun, verb, and adjective to words?
- Semantic role labeling
- Part-of-speech tagging (Correct answer)
- Coreference resolution
- Text classification
Correct answer: Part-of-speech tagging
Part-of-speech (POS) tagging labels each word in a sentence with its grammatical category such as noun, verb, adjective, or adverb.
Question 2: What is 'word sense disambiguation' in NLP?
- Converting words to their base form
- Determining which meaning of a polysemous word is used in context (Correct answer)
- Splitting compound words into components
- Translating words between languages
Correct answer: Determining which meaning of a polysemous word is used in context
Word sense disambiguation (WSD) identifies which meaning of a word with multiple meanings is intended based on surrounding context.
Question 3: In machine translation, what does BLEU score measure?
- The fluency of generated text on a 1–10 scale
- The similarity between machine-translated and reference human translations (Correct answer)
- The vocabulary size used in translations
- The inference speed of translation models
Correct answer: The similarity between machine-translated and reference human translations
BLEU (Bilingual Evaluation Understudy) measures translation quality by comparing n-gram overlap between machine output and human reference translations.
Question 4: Which technique reduces a word to its base or dictionary form, e.g., 'running' → 'run'?
- Stemming
- Tokenization
- Lemmatization (Correct answer)
- Chunking
Correct answer: Lemmatization
Lemmatization reduces a word to its canonical dictionary form (lemma) using vocabulary and morphological analysis, unlike stemming which simply chops suffixes.
Question 5: What is the primary purpose of stop word removal in NLP preprocessing?
- To correct spelling errors in text
- To eliminate common words that carry little meaningful information (Correct answer)
- To convert text to lowercase
- To segment text into sentences
Correct answer: To eliminate common words that carry little meaningful information
Stop word removal filters out very common words like 'the,' 'is,' and 'in' that typically don't contribute meaningful information for many NLP tasks.
Question 6: Which NLP task groups related words or phrases in a sentence without identifying their syntactic structure?
- Dependency parsing
- Chunking (shallow parsing) (Correct answer)
- Semantic role labeling
- Named entity recognition
Correct answer: Chunking (shallow parsing)
Chunking, also called shallow parsing, identifies and groups adjacent tokens into 'chunks' like noun phrases or verb phrases without building a full parse tree.
Question 7: What property makes GPT models 'autoregressive' in text generation?
- They process entire sequences simultaneously in both directions
- They generate each token based only on previously generated tokens (Correct answer)
- They require labeled training data for each generation task
- They use recurrent connections to process fixed-length windows
Correct answer: They generate each token based only on previously generated tokens
Autoregressive models like GPT generate text one token at a time, with each token predicted based solely on the tokens already generated to the left.
Natural Language Processing Trivia 3
Which NLP task assigns grammatical roles like noun, verb, and adjective to words?