The matrix factorization methods usually involve following steps. A row represents distribution of a particular word across all documents and a column represent distribution of all words in that document. In document-term matrix, rows represent words and column represents documents. A value in matrix Mᵢⱼ represents how many times a particular word Wᵢ occurs in context Wⱼ. In word-word matrix, rows represent words and columns refer context words. Eigen value decomposition is used for a square matrix and singular value decomposition (SVD) is used for a rectangular matrix for matrix factorization. HAL (Hyperspace Analogue to Language), LSA (Latent Semantic Analysis) are popular models that use matrix factorization. These methods factorize word-word or document-term co-occurrence matrix. GloVe model combines two widely adopted approaches for training word vectors as explained below. In case of GloVe, two types are word vector (w) and context vector (w^). Log bilinear refers to the logarithm of output is a linear regression on product of two types of word vectors. Global refers to global statistics of the training corpus. Well, I am sure you want to know what does term ‘‘ global log bilinear’’ mean. GloVe model aims to explicitly induce regularities in word vectors and finds origin of learning relationship between words. Earlier word embeddings methods such as LSA, word2vec were able to learn syntactic and semantic relationship between words but origin of it was unclear. GloVe stands for Global Vectors where global refers to global statistics of corpus and vectors are representations for words. In continuation of my word2vectors research paper explained blog, I have taken up GloVe research paper for explaining my understanding about a very detailed and comprehensive research paper.
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