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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 141–150 of 4002 papers

TitleStatusHype
Contextual Word Representations: A Contextual IntroductionCode1
Cooperative Self-training of Machine Reading ComprehensionCode1
Circumventing Concept Erasure Methods For Text-to-Image Generative ModelsCode1
Cycle Text-To-Image GAN with BERTCode1
Debiasing Pre-trained Contextualised EmbeddingsCode1
DeCLUTR: Deep Contrastive Learning for Unsupervised Textual RepresentationsCode1
A Neural Few-Shot Text Classification Reality CheckCode1
Deep Semantic-Visual Alignment for Zero-Shot Remote Sensing Image Scene ClassificationCode1
DeFINE: DEep Factorized INput Token Embeddings for Neural Sequence ModelingCode1
Combining Static Word Embeddings and Contextual Representations for Bilingual Lexicon InductionCode1
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