Short answers, pulled from the story.
GloVe stands for Global Vectors and was introduced by researchers at Stanford University in 2014. This open-source algorithm captures global corpus statistics directly within its model architecture to map words into a meaningful space.
The method relies on aggregated global word-word co-occurrence statistics from large text corpora to define statistical regularity through probabilities between specific word pairs. It combines features from global matrix factorization and local context window methods using multinomial logistic regression.
Each word possesses four trained parameters where only two hold predictive power while the other two remain irrelevant. Empirical testing showed that combining these relevant parameters as the final representation vector performed better than using either parameter alone.
The unsupervised algorithm failed to identify homographs sharing spelling but differing meanings because it calculated a single set of vectors regardless of context. Systems like SpaCy adopted this approach despite inherent limitations regarding handling multiple meanings for identical morphological structures.
Modern state-of-the-art approaches now rely on Transformer-based models like BERT which add multiple neural-network attention layers atop traditional frameworks. These advanced systems handle context and nuance far beyond what simple co-occurrence statistics could achieve alone.