Word Embeddings
Learn about word embeddings and how to generate them using Python.
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Introduction
Word embeddings are vector representations of words in a continuous space. They map words to numerical vectors when preparing data for further analysis. These vector representations help us address challenges related to word meaning, context, relationships, and ambiguity that other text representation techniques, such as BoW and TF-IDF, might not address.
Importance of word embeddings
Word embeddings are crucial for several reasons:
Semantic meaning: They capture the semantic meaning of words, i.e., words with similar meanings are represented as vectors close together in the embedding space.
Contextual information: They capture contextual information, i.e., words often appearing in similar contexts will have similar vector representations.
Dimensionality ...