NLP Concepts DY0-001 Practice Questions (Page 6)

Questions 26–30

1. 26expert · hard

   A media monitoring firm wants to classify news articles as 'positive', 'negative', or 'neutral' toward a specific company. They have 2,000 labeled articles. They tried a simple keyword-based sentiment model, but it fails on sarcasm and context (e.g., 'great, another data breach'). They want to improve accuracy. What should they do?

   • AUse TF-IDF with a logistic regression classifier
   • BApply topic modeling to identify sentiment topics
   • CExpand the keyword list with more positive and negative words
   • DTrain a neural network using word embeddings and the labeled articles
   Reveal Solution Discussion Select an answer first
2. 27application · medium

   A retail company wants to automatically categorize customer reviews as positive, negative, or neutral. They have 5,000 labeled reviews and want to build a model that understands context, such as 'not good' being negative. Which approach should they use?

   • ATrain a sentiment analysis model using word embeddings and a neural network
   • BApply TF-IDF with a rule-based threshold
   • CUse topic modeling to find sentiment clusters
   • DUse a simple keyword counter that counts positive and negative words
   Reveal Solution Discussion Select an answer first
4. 28expert · hard

   A news organization wants to classify articles into categories (sports, politics, technology). They have 10,000 labeled articles. They notice that the word 'game' appears in both sports and technology articles, but with different meanings. They want a model that can distinguish these contexts. Which approach should they use?

   • ATF-IDF with unigrams only
   • BA rule-based system that checks for the word 'game'
   • CWord embeddings with a classifier that considers surrounding words
   • DTopic modeling with 2 topics
   Reveal Solution Discussion Select an answer first
5. 29expert · hard

   A search engine for a code repository wants to index code comments so that developers can search for concepts like 'authentication' and find comments about 'login'. The comments contain code snippets, special characters, and mixed case. Which preprocessing approach should they use?

   • ATokenize only on whitespace and keep code snippets
   • BKeep the original case and use exact string matching
   • CLowercase the text, remove code snippets, and use word embeddings for semantic search
   • DRemove all punctuation and use TF-IDF with unigrams
   Reveal Solution Discussion Select an answer first
6. 30application · medium

   A legal firm has 10,000 contracts and wants to group them by the types of clauses they contain (e.g., confidentiality, indemnification, termination). They do not have labeled examples of each clause type. Which technique should they use?

   • ATopic modeling with Latent Dirichlet Allocation (LDA)
   • BSentiment analysis on each contract
   • CMachine translation to a standard language
   • DSupervised text classification with logistic regression
   Reveal Solution Discussion Select an answer first