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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 1141–1150 of 4002 papers

TitleStatusHype
Differentiable Disentanglement Filter: an Application Agnostic Core Concept Discovery Probe—0
Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder—0
Differential Privacy and Natural Language Processing to Generate Contextually Similar Decoy Messages in Honey Encryption Scheme—0
Diffusion-EXR: Controllable Review Generation for Explainable Recommendation via Diffusion Models—0
DLRG@DravidianLangTech-ACL2022: Abusive Comment Detection in Tamil using Multilingual Transformer Models—0
Di-LSTM Contrast : A Deep Neural Network for Metaphor Detection—0
Directional Skip-Gram: Explicitly Distinguishing Left and Right Context for Word Embeddings—0
Dirichlet-Smoothed Word Embeddings for Low-Resource Settings—0
Disambiguated skip-gram model—0
DFKI-MLT System Description for the WMT18 Automatic Post-editing Task—0
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