Word vectors can be applied to word analogy tasks. A word analogy completion task is defined by the relationship $$a:b::c:d$$, meaning "$$a$$ is to $$b$$ as $$c$$ is to $$d$$". Given the first three words $$a$$, $$b$$, and $$c$$, the objective is to find the missing word $$d$$. If we denote the numerical representation of a word $$w$$ as $$\textrm{vec}(w)$$, we can complete the analogy by evaluating the vector arithmetic $$\textrm{vec}(c) + \textrm{vec}(b) - \textrm{vec}(a)$$ and finding the word in the vocabulary whose vector is most similar to this result.

Word Analogy Task

Once pretrained word vectors are loaded, their encoded semantics can be demonstrated and evaluated through downstream tasks. Two fundamental tasks used to assess the quality of these representations are word similarity, which measures the geometric distance between vectors of related words, and word analogy, which uses vector arithmetic to evaluate relational knowledge.

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To utilize pretrained embeddings like GloVe or fastText, they must be parsed and loaded into memory using a dedicated data structure, such as a token embedding class. This structure processes the precomputed text files to create a vocabulary dictionary mapping each token to a unique integer index, alongside a tensor mapping these indices to their corresponding continuous vector representations. A special unknown token is consistently assigned to index 0 to handle out-of-vocabulary words encountered during subsequent processing.

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