This week, we will continue our exploration of vector space semantics and embeddings. We'll begin the module by wrapping up word embeddings and discussing bias in vector space models. Then, we'll discuss a variety of goals that any representation of word meaning should aim to achieve. These six goals will help us understand different aspects of word meaning and the relationships of words with other words. Then, we'll pivot to a coding demo that will provide you with a hands on experience working with vector space models and see how word embeddings can be used to retrieve words with similar meaning and to solve word analogy tasks.

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