Word and sentence embeddings are essential for Natural Language Processing (NLP) systems as they allow text to be processed by machine learning models. Embedding vectors are projections in a vector space of a piece of text, enabling text to be represented numerically for machine processing. This process is crucial for NLP tasks such as sentiment analysis, text classification, and language translation. Embedding vectors play a vital role in converting text data into a format that can be understood and analyzed by machine learning algorithms.
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Embedding Vectors in NLP: Word and Sentence Embeddings Explained #NLP
Preeti
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