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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 2801–2810 of 4002 papers

TitleStatusHype
WordRep: A Benchmark for Research on Learning Word Representations—0
Words are Vectors, Dependencies are Matrices: Learning Word Embeddings from Dependency Graphs—0
Word Semantic Representations using Bayesian Probabilistic Tensor Factorization—0
Word Sense Disambiguation for 158 Languages using Word Embeddings Only—0
Word Sense Distance in Human Similarity Judgements and Contextualised Word Embeddings—0
Word Sense Filtering Improves Embedding-Based Lexical Substitution—0
Word Sense Induction using Knowledge Embeddings—0
Word sense induction using word embeddings and community detection in complex networks—0
Word Sense Induction with Knowledge Distillation from BERT—0
Word-specific tonal realizations in Mandarin—0
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