Which approach fills in the target word by using surrounding context words?

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Multiple Choice

Which approach fills in the target word by using surrounding context words?

Using surrounding context words to predict the target word is what Continuous Bag of Words does. In CBOW, the words around a missing or target position are treated as input; their embeddings are combined (typically averaged) to form a context vector, which the model then uses to predict which word belongs in the target position. The training objective is to maximize the likelihood of the actual center word given its context, effectively teaching the model to infer a word from its neighbors. This contrasts with skip-gram, which starts from the center word to predict its surrounding words. Doc2Vec extends this idea to produce a vector for entire documents, while Latent Semantic Analysis uses a matrix factorization approach on word-document co-occurrence to uncover latent topics. Thus, the approach that fills in the target word from surrounding context is Continuous Bag of Words.

Using surrounding context words to predict the target word is what Continuous Bag of Words does. In CBOW, the words around a missing or target position are treated as input; their embeddings are combined (typically averaged) to form a context vector, which the model then uses to predict which word belongs in the target position. The training objective is to maximize the likelihood of the actual center word given its context, effectively teaching the model to infer a word from its neighbors. This contrasts with skip-gram, which starts from the center word to predict its surrounding words. Doc2Vec extends this idea to produce a vector for entire documents, while Latent Semantic Analysis uses a matrix factorization approach on word-document co-occurrence to uncover latent topics. Thus, the approach that fills in the target word from surrounding context is Continuous Bag of Words.

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