Which NLP technique maps each word to a dense vector where semantic…

Question

Which NLP technique maps each word to a dense vector where semantically similar words are geometrically close in the vector space?

Accepted Answer

Word embeddings (e.g., Word2Vec, GloVe) learning dense continuous vector representations — Here's the deal — c is correct because word embeddings like Word2Vec and GloVe learn dense, low-dimensional vectors where geometrically similar positions correspond to semantically related words. A (one-hot) is sparse and captures no semantic similarity.

Answer

One-hot encoding representing each word as a sparse binary vector

Answer

Bag-of-words representation counting word occurrences per document

Answer

TF-IDF weighting scaling term frequencies by inverse document frequency

Which NLP technique maps each word to a dense vector where semantically similar words are geometrically close in the vector space?

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