How can NLP systems mitigate the challenge of cultural nuances and idiomatic expressions?

By ignoring context and focusing on individual words

By using only rule-based grammar systems

By training on diverse, culturally representative datasets

By removing all non-English words from the training data

A team is building a text classifier for social media posts. They notice that the model performs poorly because words like 'u' and 'you' are treated as different features. Which preprocessing step would directly address this issue?

Normalize informal variants to their standard forms using a slang dictionary.

Remove all punctuation from the posts.

Increase the n-gram range from 1 to 3.

In text preprocessing, what is the purpose of stop-word removal?

To correct spelling errors in the text

To eliminate common words that add little semantic value, such as 'the' and 'and'

To replace all words with their base or dictionary form

To split text into individual sentences or words

A global customer-service chatbot is being trained on English text. Users from different regions use phrases like 'I'm gutted' (UK) and 'I'm pumped' (US) to express opposite sentiments. The chatbot misclassifies these phrases. Which approach would best mitigate this cultural nuance?

Remove all slang words from the input before classification.

Use a single global model without any regional adaptation.

Apply aggressive stemming to all words.

Fine-tune the model on region-specific labeled data that includes these phrases.

EC-CouncilArtificial Intelligence Essentials

Domain 3Objective 4

Natural Language Processing (NLP) AIE Practice Questions (Page 7)

Part of the Building Blocks of AI domain, which makes up ~25% of our current practice bank.

38questions here

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5concepts

Questions 31–35

31foundation · easy
How can NLP systems mitigate the challenge of cultural nuances and idiomatic expressions?
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32application · easy
A legal firm wants to automatically extract party names, dates, and monetary amounts from contracts to populate a case-management database. Which NLP application should the firm use?
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33application · medium
A team is building a text classifier for social media posts. They notice that the model performs poorly because words like 'u' and 'you' are treated as different features. Which preprocessing step would directly address this issue?
Select an answer first
34foundation · easy
In text preprocessing, what is the purpose of stop-word removal?
Select an answer first
35expert · hard
A global customer-service chatbot is being trained on English text. Users from different regions use phrases like 'I'm gutted' (UK) and 'I'm pumped' (US) to express opposite sentiments. The chatbot misclassifies these phrases. Which approach would best mitigate this cultural nuance?
Select an answer first

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