What does GloVe stand for in machine learning?
GloVe stands for Global Vectors. It is a model for distributed word representation developed as an open-source project at Stanford University and launched in 2014.
Questions about GloVe
Short answers, pulled from the story.
How does the GloVe algorithm learn word representations?
GloVe trains on aggregated global word-word co-occurrence statistics from a corpus. It learns two vectors per word so that the ratio of co-occurrence probabilities between word pairs is captured in the geometry of the vector space.
What is the difference between GloVe and word2vec?
GloVe was designed as a competitor to word2vec and the original paper noted multiple improvements. GloVe combines global matrix factorization and local context window methods, whereas word2vec relies primarily on local context windows.
What are the limitations of GloVe for natural language processing?
GloVe cannot distinguish homographs, that is, words with the same spelling but different meanings. The algorithm assigns a single vector to each word form regardless of context, so different senses of a word are merged into one representation.
What replaced GloVe as the state of the art in NLP?
Transformer-based models such as BERT replaced GloVe and word2vec as the state of the art in natural language processing. These models add multiple neural-network attention layers on top of a word embedding approach, allowing context-sensitive representations.
What real-world applications has GloVe been used for?
GloVe has been used to find relations between words such as synonyms, company-product pairs, and zip code-to-city mappings. It was also used as the word representation framework in systems designed to detect psychological distress in patient interviews, and the SpaCy library uses it to build semantic word embedding features.