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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 711–720 of 4002 papers

TitleStatusHype
Dialects Identification of Armenian Language—0
A General Framework for Detecting Metaphorical Collocations—0
BERTrade: Using Contextual Embeddings to Parse Old French—0
Don’t Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddings—0
Enhancing Deep Learning with Embedded Features for Arabic Named Entity RecognitionCode0
Pre-trained Models or Feature Engineering: The Case of Dialectal Arabic—0
Using Convolution Neural Network with BERT for Stance Detection in Vietnamese—0
XLNET-GRU Sentiment Regression Model for Cryptocurrency News in English and Malay—0
Don't Forget Cheap Training Signals Before Building Unsupervised Bilingual Word Embeddings—0
Leveraging Dependency Grammar for Fine-Grained Offensive Language Detection using Graph Convolutional NetworksCode0
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