Deep Learning with Keras by Antonio Gulli & Sujit Pal

Author:Antonio Gulli & Sujit Pal [Gulli, Antonio]
Language: eng
Format: azw3
Tags: COM018000 - COMPUTERS / Data Processing, COM044000 - COMPUTERS / Neural Networks, COM004000 - COMPUTERS / Intelligence (AI) & Semantics
Publisher: Packt Publishing
Published: 2017-04-26T04:00:00+00:00

Exploring GloVe

The global vectors for word representation, or GloVe, embeddings was created by Jeffrey Pennington, Richard Socher, and Christopher Manning (for more information refer to the article: GloVe: Global Vectors for Word Representation, by J. Pennington, R. Socher, and C. Manning, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Pp. 1532–1543, 2013). The authors describe GloVe as an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space.

GloVe differs from word2vec in that word2vec is a predictive model while GloVe is a count-based model. The first step is to construct a large matrix of (word, context) pairs that co-occur in the training corpus. Each element of this matrix represents how often a word represented by the row co-occurs in the context (usually a sequence of words) represented by the column, as shown in the following figure:

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