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Questions and Answers
Which method is suggested as an alternative to one-hot encoding for word representation in NLP?
Which method is suggested as an alternative to one-hot encoding for word representation in NLP?
- Generalization from known associations
- High-dimensional feature vectors (correct)
- t-SNE
- Word clustering
What is the purpose of word embeddings in NLP?
What is the purpose of word embeddings in NLP?
- To represent words using one-hot encoding
- To visualize word clusters
- To capture relationships between words
- To enable algorithms to recognize similarities between words (correct)
What aspects of words can be captured by word embeddings?
What aspects of words can be captured by word embeddings?
- Color, shape, and size
- Taste, smell, and texture
- Gender, royalty, and age (correct)
- Temperature, weight, and density
Which visualization technique is mentioned in the text for visualizing word embeddings?
Which visualization technique is mentioned in the text for visualizing word embeddings?
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What is the significance of word embeddings in NLP?
What is the significance of word embeddings in NLP?
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Study Notes
Word Representation in NLP
- Word embeddings are suggested as an alternative to one-hot encoding for word representation in NLP.
Purpose of Word Embeddings
- The purpose of word embeddings is to capture the meaning and context of words in a way that can be understood by machines.
Aspects of Words Captured by Word Embeddings
- Word embeddings can capture various aspects of words, including semantic meaning, grammatical context, and relationships between words.
Visualizing Word Embeddings
- T-SNE (t-Distributed Stochastic Neighbor Embedding) is a visualization technique mentioned for visualizing word embeddings.
Significance of Word Embeddings
- Word embeddings are significant in NLP as they enable machines to understand the nuances of language, allowing for better performance in tasks such as text classification, sentiment analysis, and language translation.
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