Embeddings transform complicated text data into simpler numerical representations, allowing algorithms to perform tasks like accurately comparing the similarity of words and sentences.
One-hot encoding is the simplest form of embeddings but fails to capture semantic relationships. Advanced pre-trained embeddings like Word2Vec and GloVe improve this but lack context comprehension.
The latest advancements in embedding technology, such as those produced by BERT and OpenAI, offer contextual embeddings that dynamically adjust to the meanings based on sentence context.
Embeddings are crucial in NLP as they allow machines to read and understand human language through numerical representations, serving as the foundation for various applications.
#embeddings #natural-language-processing #machine-learning #text-classification #contextual-understanding
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