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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 1341–1350 of 4002 papers

TitleStatusHype
Cooperative Self-training of Machine Reading Comprehension—0
Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings—0
Convolutional Sentence Kernel from Word Embeddings for Short Text Categorization—0
Attention-based Semantic Priming for Slot-filling—0
Convolutional Neural Networks for Sentiment Analysis on Weibo Data: A Natural Language Processing Approach—0
Attention-based model for predicting question relatedness on Stack Overflow—0
Convolutional Neural Networks for Sentiment Classification on Business Reviews—0
Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings—0
Attending to Characters in Neural Sequence Labeling Models—0
Analyzing autoencoder-based acoustic word embeddings—0
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