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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 3701–3710 of 4002 papers

TitleStatusHype
Do Not Harm Protected Groups in Debiasing Language Representation Models—0
Do not neglect related languages: The case of low-resource Occitan cross-lingual word embeddings—0
Don't Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddings—0
Don’t Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddings—0
Don’t Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddings—0
Do Nuclear Submarines Have Nuclear Captains? A Challenge Dataset for Commonsense Reasoning over Adjectives and Objects—0
DoTheMath at SemEval-2020 Task 12 : Deep Neural Networks with Self Attention for Arabic Offensive Language Detection—0
Do We Need Neural Models to Explain Human Judgments of Acceptability?—0
The Influence of Down-Sampling Strategies on SVD Word Embedding Stability—0
Do Word Embeddings Really Understand Loughran-McDonald's Polarities?—0
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