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The paper highlights two main architectures for learning word embeddings:

: It describes the Skip-gram and Continuous Bag-of-Words (CBOW) models, which allow for the computation of high-quality word vectors from massive datasets [1, 2].

: Predicts a target word based on its surrounding context.

The Skip-gram model, depicted above, is generally more effective for larger datasets and infrequent words, while CBOW is faster to train [1].

The paper highlights two main architectures for learning word embeddings:

: It describes the Skip-gram and Continuous Bag-of-Words (CBOW) models, which allow for the computation of high-quality word vectors from massive datasets [1, 2].

: Predicts a target word based on its surrounding context.

The Skip-gram model, depicted above, is generally more effective for larger datasets and infrequent words, while CBOW is faster to train [1].