AI Associate Data for AI Practice Question
A company is building a chatbot using Einstein Bot's AI capabilities. They want to train intent recognition using historical chat transcripts. The transcripts contain many typos (e.g., 'hellp' instead of 'help') and slang (e.g., 'gonna' instead of 'going to'). The initial model performs poorly, misclassifying many intents. What data cleaning step is most important?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Normalize text by applying spell-correction and replacing slang with standard terms.
Normalizing text by correcting common typos and expanding slang reduces vocabulary sparsity and helps the model learn consistent word associations, improving intent recognition.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a spell-checker only for words that appear infrequently.
Why it's wrong here
Partial spell-checking may miss many issues; comprehensive normalization is more effective.
- ✗
Keep the raw text as is because it reflects real user behavior.
Why it's wrong here
Raw text with typos and slang increases sparsity and makes it harder for the model to generalize.
- ✓
Normalize text by applying spell-correction and replacing slang with standard terms.
Why this is correct
Normalization reduces noise and variability, enabling the model to focus on meaningful patterns.
- ✗
Remove all messages that contain typos or slang to clean the dataset.
Why it's wrong here
Removing many messages reduces dataset size and may discard useful patterns; normalization is preferred.
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