The skip-gram model of word2vec primarily aims to predict what?

The skip-gram model of word2vec is designed to predict surrounding words based on a given center word. Its main objective is to learn word representations by maximizing the prediction of context words surrounding a specific target word within a defined window size. This ability to capture the relationships and similarities between words is what allows the model to create rich vector representations of words.

In essence, by taking a central word as input, the skip-gram model tries to find and output predictions for the words that are likely to occur in the context of that center word. This approach effectively harnesses large amounts of textual data, enabling the model to learn nuanced word embeddings based on their co-occurrence patterns in the text.

The other choices do not accurately describe the primary function of the skip-gram model. For instance, predicting the overall sentiment of the text focuses on a different aspect of language processing than merely predicting context words. Understanding these distinctions is essential for grasping how various models and architectures in natural language processing operate.