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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 2071–2080 of 4002 papers

TitleStatusHype
Refining Pretrained Word Embeddings Using Layer-wise Relevance Propagation—0
Refining Word Embeddings for Sentiment Analysis—0
Regional Differences in Information Privacy Concerns After the Facebook-Cambridge Analytica Data Scandal—0
Regional Negative Bias in Word Embeddings Predicts Racial Animus--but only via Name Frequency—0
Regular polysemy: from sense vectors to sense patterns—0
Rehabilitation of Count-based Models for Word Vector Representations—0
Reinforcing the Topic of Embeddings with Theta Pure Dependence for Text Classification—0
Relating Word Embedding Gender Biases to Gender Gaps: A Cross-Cultural Analysis—0
Relation Extraction Datasets in the Digital Humanities Domain and their Evaluation with Word Embeddings—0
Relation Extraction: Perspective from Convolutional Neural Networks—0
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