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Related Questions
- What is the difference between word embeddings and traditional word representations in NLP?
- How do word embeddings like Word2Vec and GloVe reduce the dimensionality of text data?
- Can you explain the concept of semantic meaning in word embeddings and how it helps in NLP tasks?
- How do word embeddings handle out-of-vocabulary words and unknown words in text data?
- What are the applications of word embeddings in natural language processing tasks such as text classification and sentiment analysis?
- How do word embeddings help in reducing the curse of dimensionality in text data?
- Can you compare and contrast different word embedding techniques such as Word2Vec, GloVe, and FastText?
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