Natural Language Processing (NLP) AIE Practice Questions (Page 2)

Questions 6–10

1. 6expert · hard

   A social media analytics company is analyzing user-generated content to identify trending topics. The content includes hashtags, emojis, and misspellings. The team wants to reduce noise while preserving the meaning of the text. They are considering: (1) applying aggressive stemming, (2) using lemmatization with a custom dictionary for slang, (3) removing all non-alphanumeric characters, (4) using a pre-trained word embedding model. Which approach is most likely to balance noise reduction and meaning preservation?

   • AUse lemmatization with a custom dictionary for slang and misspellings.
   • BRemove all non-alphanumeric characters.
   • CApply aggressive stemming to all words.
   • DUse a pre-trained word embedding model without any preprocessing.
   Reveal Solution Discussion Select an answer first
2. 7application · medium

   A global e-commerce company wants to deploy a chatbot that can understand customer queries in multiple languages. The chatbot often misinterprets idiomatic expressions and cultural references. Which strategy would most effectively mitigate these challenges?

   • ATrain the chatbot on a diverse multilingual corpus that includes idiomatic expressions and cultural contexts.
   • BUse a simple keyword-matching algorithm to identify intents.
   • CTranslate all queries to English before processing.
   • DRemove all stop words from the input before processing.
   Reveal Solution Discussion Select an answer first
4. 8expert · hard

   A news aggregator wants to automatically identify all mentions of companies and their stock tickers in articles. The system must also handle cases where the same company is referred to by different names (e.g., 'Apple' vs. 'AAPL'). Which combination of NLP tasks is required?

   • AMachine translation followed by text summarization.
   • BSentiment analysis followed by topic modeling.
   • CNamed entity recognition followed by entity linking to a knowledge base.
   • DPart-of-speech tagging followed by stemming.
   Reveal Solution Discussion Select an answer first
5. 9application · easy

   A data science team is building a spam filter for emails. They want to reduce the noise from common words like 'the', 'and', and 'for' so that the model focuses on content words. Which preprocessing step should they apply?

   • APart-of-speech tagging
   • BStop-word removal
   • CLemmatization
   • DTokenization
   Reveal Solution Discussion Select an answer first
6. 10foundation · easy

   Which NLP application would be most appropriate for automatically identifying all mentions of company names and locations in a news article?

   • ANamed entity recognition
   • BSentiment analysis
   • CText summarization
   • DLanguage modeling
   Reveal Solution Discussion Select an answer first