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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 2881–2890 of 4002 papers

TitleStatusHype
MITRE: Seven Systems for Semantic Similarity in Tweets—0
Mixed Membership Word Embeddings for Computational Social Science—0
Mixing syntagmatic and paradigmatic information for concept detection—0
Mixtures of Deep Neural Experts for Automated Speech Scoring—0
Model-based Word Embeddings from Decompositions of Count Matrices—0
Model Choices Influence Attributive Word Associations: A Semi-supervised Analysis of Static Word Embeddings—0
Model-Free Context-Aware Word Composition—0
Modeling Context Words as Regions: An Ordinal Regression Approach to Word Embedding—0
Modeling Noisiness to Recognize Named Entities using Multitask Neural Networks on Social Media—0
Modeling Order in Neural Word Embeddings at Scale—0
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