Re:Show HN: Wikipedia2Vec – A tool for learning embeddings o...
by lightbyte on Thursday August 02, 2018 @01:56PM
(#17673735)
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.
>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.