AWS Certified AI PractitionerAI/ML and Generative AI FundamentalsMedium
A data scientist is preparing a dataset of customer reviews for sentiment analysis using a generative AI model. They need to represent each word in the reviews as a numerical vector that captures its semantic meaning and contextual relationships with other words. Which technique is best suited for this purpose?
- AFeature scaling
- BOne-hot encoding
- CPrincipal Component Analysis (PCA)
- DWord embeddings
Show answer & explanationAnswer & explanation
Correct answer: D. Word embeddings
Word embeddings are dense vector representations of words that capture their semantic relationships and contextual meanings. Words with similar meanings or that appear in similar contexts will have similar embedding vectors. This is crucial for generative AI models to understand the nuances of language in sentiment analysis, unlike one-hot encoding which treats each word as independent.
Why the other options are wrong
- A. Feature scaling is for numerical features and doesn't apply to representing words semantically.
- B. One-hot encoding creates sparse, high-dimensional vectors that don't capture semantic relationships between words.
- C. PCA is a dimensionality reduction technique, not a method for initially representing words with semantic meaning.
Word Embeddings
Dense vector representations of words that capture their semantic meanings and contextual relationships, allowing models to understand linguistic nuances.
- Words with similar meanings have similar vector representations.
- Reduces dimensionality compared to one-hot encoding.
- Learned from large text corpora.
- Essential for modern NLP tasks, including generative AI language models.
Memory trick: Embed words to understand their meaning, not just their ID.