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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 3491–3500 of 4002 papers

TitleStatusHype
Visualizing Linguistic Shift—0
Word and Document Embeddings based on Neural Network Approaches—0
Zero-Shot Visual Question Answering—0
A Semi-supervised Framework for Image CaptioningCode0
Attending to Characters in Neural Sequence Labeling Models—0
Multi-view Recurrent Neural Acoustic Word Embeddings—0
A Comparison of Word Embeddings for English and Cross-Lingual Chinese Word Sense Disambiguation—0
Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and DocumentsCode0
Discriminative Acoustic Word Embeddings: Recurrent Neural Network-Based Approaches—0
Automated Generation of Multilingual Clusters for the Evaluation of Distributed RepresentationsCode0
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