What are pretrained word embeddings in neural network

This recipe explains what are pretrained word embeddings in neural network

Recipe Objective - What are pretrained word embeddings in neural network?

Pretrained word embeddings are generally computed using the word-occurrence statistics that is observations about what words co-occur in the sentences or documents with using a variety of techniques like neural networks etc. Pretrained word embeddings provides loading from a precomputed embedding space that is highly structured and also exhibits useful properties that is it captures the generic aspects of language structure. The idea of a dense and low-dimensional embedding space for words that is computed in an unsupervised way was initially explored by Bengio et al. in early 2000s. The rationale behind using the pretrained word embeddings in natural language processing neural networks is the same as for using pretrained convnets in image classification neural networks that is its fill the void of unavoidability of data to learn truly powerful features on its own and feature be generic in nature.

This recipe explains what are pretrained word embeddings, similarities between pretrained word embeddings and pretrained convnets and how it is exceuted.

Learn How to Build a Multi Class Text Classification Model using BERT

Explanation of pretrained word embedding.

Word2Vec algorithm developed by Tomas Mikolov at Google in 2013 captures the specific semantic properties such as gender is a pretrained word embedding widely used in the industry. Another popular one is Global Vectors for Word Representation (GloVe) which was developed by the Stanford researchers in 2014. GloVe embedding technique is based on factorizing a matrix of the word co-occurrence statistics. Word2Vec and GloVe can be easily used using the Keras framework.

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