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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 791–800 of 4002 papers

TitleStatusHype
Topological Data Analysis for Word Sense Disambiguation—0
SemSup: Semantic Supervision for Simple and Scalable Zero-shot GeneralizationCode0
Prediction of Depression Severity Based on the Prosodic and Semantic Features with Bidirectional LSTM and Time Distributed CNN—0
Self-Attention for Incomplete Utterance Rewriting—0
Domain Disentangled Generative Adversarial Network for Zero-Shot Sketch-Based 3D Shape Retrieval—0
Sobolev Transport: A Scalable Metric for Probability Measures with Graph MetricsCode0
Seeing the advantage: visually grounding word embeddings to better capture human semantic knowledge—0
Data-Driven Mitigation of Adversarial Text Perturbation—0
Selection Strategies for Commonsense Knowledge—0
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision—0
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