- Word Embeddings: This is a more advanced technique that represents words as dense vectors in a high-dimensional space. Word embeddings, such as Word2Vec and GloVe, capture the semantic meaning of words and their relationships with each other. Word embeddings are often used for tasks such as language modeling, text classification, and machine translation.
- Doc2Vec: This is a variant of Word2Vec that represents documents as vectors, rather than individual words. Doc2Vec is often used for tasks such as document classification, clustering, and information retrieval.
- Sentence Embeddings: This is a technique that represents sentences as vectors, rather than individual words or documents. Sentence embeddings are often used for tasks such as sentence classification, sentiment analysis, and machine translation.
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