Arguably the most important application of machine learning in text analysis, the Word2Vec algorithm is both a fascinating and very useful tool. As the name suggests, it creates a vector representation of words based on the corpus we are using. But the magic of Word2Vec is in how it manages to capture the semantic representation of words in a vector. The papers, Efficient Estimation of Word Representations in Vector Space [1] [Mikolov and others, 2013], Distributed Representations of Words and Phrases and their Compositionality [2] [Mikolov and others, 2013], and Linguistic Regularities in Continuous Space Word Representations [3] [Mikolov and others, 2013] lay the foundations for Word2Vec and describe their uses.
We've mentioned that these word vectors help represent the semantics of words – what exactly does this mean? Well for starters, it means we could use vector reasoning for these words – one of the most famous examples is from Mikolov's paper, where we see that if we use...
