Words Representation in Vector Space

code
Published

November 2022

Word2vec explained.

Original paper T.Mikolov, K.Chen, G.Corrado, J.Dean Efficient Estimation of Word Representations in Vector Space

word_vectors.most_similar(positive=['Paris', 'Spain'], negative=['France'])
[('Madrid', 0.8625079989433289),
 ('Barcelona', 0.7637037634849548),
 ('Sevilla', 0.6874054074287415),
 ('Seville', 0.6747831106185913),
 ('Malaga', 0.6494932174682617),
 ('Zaragoza', 0.6459373831748962),
 ('Valencia', 0.6383104920387268),
 ('Alicante', 0.6115807890892029),
 ('Salamanca', 0.6041630506515503),
 ('Murcia', 0.6019026041030884)]
Source: Word2vec

Thanks to Jiří Materna for Machine Learning College in Prague. Where you can find much more handful notebooks to understand the basics of machine learning.