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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 561–570 of 4002 papers

TitleStatusHype
Cross-Lingual Word Representations via Spectral Graph EmbeddingsCode0
Acquiring Common Sense Spatial Knowledge through Implicit Spatial TemplatesCode0
Crossmodal ASR Error Correction with Discrete Speech UnitsCode0
Debiasing Convolutional Neural Networks via Meta OrthogonalizationCode0
An Unsupervised Neural Attention Model for Aspect ExtractionCode0
A Hybrid Approach for Aspect-Based Sentiment Analysis Using Deep Contextual Word Embeddings and Hierarchical AttentionCode0
Cross-lingual Models of Word Embeddings: An Empirical ComparisonCode0
Contextually Propagated Term Weights for Document RepresentationCode0
Cross-Lingual BERT Transformation for Zero-Shot Dependency ParsingCode0
Acoustic word embeddings for zero-resource languages using self-supervised contrastive learning and multilingual adaptationCode0
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