The word analogy completion task can be implemented programmatically by defining a function, such as get_analogy, that takes the first three text tokens ($a$, $b$, and $c$) along with a pretrained embedding object. It extracts the numerical vectors for the three words, evaluates the expression $\textrm{vec}(c) + \textrm{vec}(b) - \textrm{vec}(a)$, and uses a nearest neighbors function (knn) to find the most similar vector in the embedding matrix, returning its corresponding word.

def get_analogy(token_a, token_b, token_c, embed):
    vecs = embed[[token_a, token_b, token_c]]
    x = vecs[1] - vecs[0] + vecs[2]
    topk, cos = knn(embed.idx_to_vec, x, 1)
    return embed.idx_to_token[int(topk[0])]  # Remove unknown words