King – man + woman is queen; but why? (2017) (p.migdal.pl)

The article revisits the fundamental mechanics of word2vec, a pivotal algorithm in natural language processing (NLP) that converts words into numerical vectors. By doing so, it places semantically similar words in proximity within a multi-dimensional space, allowing for insightful vector arithmetic that can reveal analogies, such as the well-known example where "king - man + woman = queen." The significance of this approach lies in its capacity to uncover underlying linguistic patterns and relationships, which can enhance various applications, from recommendation systems to improving human-computer interaction. The author explains key concepts such as pointwise mutual information (PMI) and the distributional hypothesis, emphasizing how the relationships between words can be explored using their co-occurrences. Additionally, the article discusses dimensionality reduction techniques that allow for efficient representation of vocabulary while preserving essential contextual relationships, which can, however, lead to unintended biases. Overall, this exploration highlights the sophisticated interplay of machine learning, cognitive science, and linguistics, urging the AI/ML community to be mindful of the limitations and biases inherent in training models on language data.