GLOVE: GLOBAL VECTORS FOR WORD REPRESENTATION
Abstract
GloVe (Global Vectors for Word Representation) is a widely used method for generating word embeddings, which effectively leverages global co-occurrence statistics of words in a corpus. Unlike context-based models like Word2Vec, GloVe directly utilizes word co-occurrence matrices to learn semantic relationships. This article provides a detailed theoretical explanation of the GloVe model, its training methodology, mathematical formulation, its application in Natural Language Processing (NLP), and a comparison with other embedding models.
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