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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 3471–3480 of 4002 papers

TitleStatusHype
CG-CNN: Self-Supervised Feature Extraction Through Contextual Guidance and Transfer Learning—0
Context Vectors are Reflections of Word Vectors in Half the Dimensions—0
Continuous Word Embedding Fusion via Spectral Decomposition—0
Contrastive Visual Semantic Pretraining Magnifies the Semantics of Natural Language Representations—0
Contrastive Word Embedding Learning for Neural Machine Translation—0
Convolutional Neural Network for Universal Sentence Embeddings—0
Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings—0
Convolutional Neural Networks for Sentiment Classification on Business Reviews—0
Convolutional Neural Networks for Sentiment Analysis on Weibo Data: A Natural Language Processing Approach—0
Convolutional Sentence Kernel from Word Embeddings for Short Text Categorization—0
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