If you're wondering what the point of this is when word2vec wikipedia pre-trained embeddings are already easily found >Wikipedia2Vec is based on the Word2vec

If you're wondering what the point of this is when word2vec wikipedia pre-trained embeddings are already easily found >Wikipedia2Vec is based on the Word2vec's skip-gram model that learns to predict neighboring words given each word in corpora. We extend the skip-gram model by adding the following two submodels: >The link graph model that learns to estimate neighboring entities given an entity in the link graph of Wikipedia entities. >The anchor context model that learns to predict neighboring words given an entity by using a link that points to the entity and its neighboring words. >By jointly optimizing the skip-gram model and these two submodels, our model simultaneously learns the embedding of words and entities from Wikipedia. For further details, please refer to our paper: Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation.