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My past paper describes an entity linking method based on Wikipedia2Vec: https://arxiv.org/abs/1601.01343 You need to extract entity names using an NER software (e.g., SpaCy, Stanf…

Unlike Word2vec, this tool learns embeddings of entities (i.e., entries in Wikipedia) as well as words. And although the model implemented in this tool is based on Word2vec's skip-…

You can see the visualization of the embedding vector space here: http://projector.tensorflow.org/?config=https://wikipedia2ve... I recommend to use T-SNE instead of PCA, which can…

Author here. More broadly, embedding is a mapping from objects (e.g., words and entities) to vectors of real numbers. And as described in the mcxlog's comment, an entity refers to …

Thank you for your feedback! I am also interested in conducting experiments on extrinsic tasks such as text classification. In addition to word embeddings, Wikipedia2Vec also conta…

Thanks :) 1) I think learning entity embeddings using the Doc2Vec (paragraph vector) model is an interesting idea, but we did not test it. 2) This tool was initially developed to a…

Regarding word embedding algorithm, I am interested in supporting other models that uses subword information (e.g., Fasttext). Further, there have been proposed various recent mode…

We did not add Fasttext to our benchmarks because of a minor technical issue but we will work on it. Further, to conduct a fair comparison with ELMo, I think it is needed to use ex…

The current code is written specifically for Wikipedia. However, its algorithm is portable for knowledge bases that contains articles and their entity annotations.

What kind of output did you mean? Wikipedia2Vec learns embeddings of entities which have links from other articles more than min-entity-count times.

Please note that similar to other approaches (e.g., node2vec), Wikipedia2Vec learns embeddings for Wikipedia entities in addition to embeddings for words.

When clicking on the link, Linkify displays a small widget that contains links to typical search sites such as Wikipedia, Google, Twitter, etc.