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Word Embeddings

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 )

Papers

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Showing 2761–2770 of 4002 papers

TitleStatusHype
Word Embeddings, Analogies, and Machine Learning: Beyond king - man + woman = queen—0
Word embeddings and discourse information for Quality Estimation—0
Word embeddings and recurrent neural networks based on Long-Short Term Memory nodes in supervised biomedical word sense disambiguation—0
Word Embeddings and Their Use In Sentence Classification Tasks—0
Word Embeddings and Validity Indexes in Fuzzy Clustering—0
Word Embeddings as Features for Supervised Coreference Resolution—0
Word Embeddings as Metric Recovery in Semantic Spaces—0
Word Embeddings as Tuples of Feature Probabilities—0
Word Embeddings: A Survey—0
Word Embeddings based on Fixed-Size Ordinally Forgetting Encoding—0
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