Simplify and explain.

Representing word as a continuous vector traces its history back to the last century where one of the most promising works used back-propagation error to learn the vector representations [9]. Over the years several methods were proposed and they can be categorised into two different approaches. First, the count-based approach - where some variety of global word-context co-occurrence matrix is leveraged to learn the vector representations [26, 7, 25]. Second, the shallow window-based methods - where a sliding window approach is applied on a large corpus of text and embeddings are learned by considering the context words within a predefined local window of the target word [21, 22, 17]. Both of the generalisations, on the lowest level, learn the embedding by considering multiple pairs of words. While the internal mechanics and the heuristics considered is different, overall the approaches perform the same task of training word embedding such that the linguistic relationship between the pair of words mirrors the spatial relationship between vectors embeddings of the words. The initial push for creating word embeddings was to move away from the atomic and sparse nature of words to a more continuous one, as it leads to smaller size representation of words which saves storage. Another interesting aspect is the formation of latent features which is represented by each dimension of the learned continuous word vectors. This highlights the notion that there are multiple perspectives to look at a word and a pair of words can be similar by any combination of these perspectives. For example, a word embedding could have the first dimension store the notion of royalty, second dimension the gender, third the age and so on. And two words like king and queen may be very similar based on the first dimension of royalty but completely different from the second dimension of gender. This is because while training word pairs which are usually seen together are forced to have similar vector representation.
Recent works extend this idea to highlight that these embeddings also showcase several semantic and morphological properties. This is a further generalization of latent features learning and proposes unique applications of the embeddings. For examples, word embedding can be used to find semantic similarity between words, where a simple vector similarity method like cosine similarity between the embeddings of two words also represents how similar two words are, like in
 arXiv:2012.00728v2 [cs.CL] 5 Dec 2020
the case  Provide your response in exactly 7 paragraphs (separated nearby paragraphs with 3 aterisk symbols ***).
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