natural-language-processing-12
Word Embeddings
3260 papers • 126 benchmarks • 313 datasets
Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers. Techniques for learning word embeddings can include Word2Vec, GloVe, and other neural network-based approaches that train on an NLP task such as language modeling or document classification. ( Image credit: Dynamic Word Embedding for Evolving Semantic Discovery )
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