Natural Language Processing — AI Technical Interview Questions & Answers

Natural Language Processing

Natural Language Processing

  • NLP is AI for human language: text or speech. Levels: tokens → syntax → meaning → dialogue. Tasks: classification (spam), translation, NER, Q&A, summarisation, chat. Today: transformers / large language models. Still fail on sarcasm, rare words, and invented facts (hallucination).
  • Tiny start: split “Asha got 90 marks” into tokens. That is not understanding yet. Don’t jump to GPT without saying tokens and a task.
  • Natural Language Processing — output: ['asha', 'got', '90', 'marks'] then a reminder. Classification/chat comes after.
  text
    │ tokenize
    ▼
  tokens → task
    (class / translate / chat)

Technical Interview Tip: One task + one failure mode.

Code Examples for Natural Language Processing

Example

# NLP start — tokenize
text = "Asha got 90 marks"
tokens = text.lower().split()
print(tokens)
print("tokens ≠ understanding")

Natural Language Processing — output: ['asha', 'got', '90', 'marks'] then a reminder. Classification/chat comes after.

How to Explain Natural Language Processing to an Interviewer

Explain Natural Language Processing as if you are teaching a junior — definition, then one example.

Machines + human language. Tasks : class / translate / NER / chat.

What does the example print, and what does that prove?

Natural Language Processing — output: ['asha', 'got', '90', 'marks'] then a reminder

What mistake do freshers make with Natural Language Processing?

hallucination.