How do word embeddings differ from basic vectorization in terms of semantic meaning?

Word embeddings are a type of vector representation that captures the semantic meaning and relationships between words in a way that basic vectorization methods cannot. Unlike basic vectorization techniques, such as bag-of-words or term frequency-inverse document frequency (TF-IDF), which treat words as independent and ignore their meaning in context, word embeddings place similar words closer together in a high-dimensional space based on their usage in large corpora.

This relationship allows word embeddings to represent nuances of meaning and more complex semantic structures, such as analogies (e.g., "king" is to "queen" as "man" is to "woman"). By utilizing models like Word2Vec or GloVe, word embeddings provide a rich representation, enabling tasks such as sentiment analysis, language translation, and more to leverage the context and meaning of words rather than treating them purely as individual tokens.

In contrast, basic vectorization does not encode such relationships and often leads to sparsity in the representation, which limits its effectiveness in understanding semantic meaning. Thus, word embeddings distinctly outperform basic vectorization by mapping words into a meaningful vector space that reflects their semantic similarities and relationships.