More recent algorithms extending skip-gram and CBOW

We already saw that the Word2vec techniques are quite powerful in capturing semantics of words. However, they are not without their limitations. For example, they do not pay attention to the distance between a context word and the target word. However, if the context word is further away from the target word, its impact on the target word should be less. Therefore, we will discuss techniques that pay separate attention to different positions in the context. Another limitation of Word2vec is that it only pays attention to a very small window around a given word when computing the word vector. However, in reality, the way the word co-occurs throughout a corpus should be considered to compute good word vectors. So, we will look at a technique that not only looks at the context of a word, but also at the global co-occurrence information of the word.

A limitation of the skip-gram algorithm

The previously-discussed skip-gram algorithm and all...