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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 751–760 of 4002 papers

TitleStatusHype
Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling—0
A Survey on Sentence Embedding Models Performance for Patent AnalysisCode0
Extremal GloVe: Theoretically Accurate Distributed Word Embedding by Tail Inference—0
RankMat : Matrix Factorization with Calibrated Distributed Embedding and Fairness Enhancement—0
Word Embeddings and Validity Indexes in Fuzzy Clustering—0
From Hyperbolic Geometry Back to Word EmbeddingsCode0
Approach to Predicting News -- A Precise Multi-LSTM Network With BERTCode0
Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings—0
A Survey on Word Meta-Embedding Learning—0
Towards Arabic Sentence Simplification via Classification and Generative ApproachesCode0
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